Convex Area : It measures the area of the Convex Hull. The Convex Hull is the
minimum convex area that surrounds the object. This feature is similar to the
Filled Area, however the convex hull can approximate to better areas since
most of the objects that we are interested in have a convex shape. As seen in
the data analysis section, the trolley can have non-convex shapes depending on
the pose. This feature will reduce the influence of different poses.

  * •

Solidity : The solidity returns the ratio of the area of the object by its
Convex Hull area. Equation A.5 shows how it is computed where $C$ is the
convex area and $A$ is the area of the object. This feature represents how
convex is our object of interest.

$S=\frac{A}{C}$ (A.5)

  * •

Minor Axis Length : The minor axis length approximates the object to an
ellipse format and captures the minor axis length. This feature captures
ellipse information from the object. Since we can approximate our object as an
ellipse, it can differentiate various objects using this feature.

  * •

Major Axis Length : This feature is similar to the previous one, but this one
captures the major axis length instead of the minor axis length.

  * •

Eccentricity : The eccentricity measures the ratio between the focus of the
ellipse and the major axis length. It returns a value between 0 and 1, where 0
is a circle and 1 is a line. It approximates the best ellipse into region of
the object. This kind of feature can distinguish easily between a trolley and
a bike. Figure A.10 shows different types of eccentricity. Equation A.6 shows
how to compute the eccentricity where $c$ is the focal point length, $a$ is
the major axis length and $b$ is the minor axis length.

$\displaystyle c^{2}=a^{2}-b^{2}$ (A.6) $\displaystyle e=\frac{c}{a}$

Figure A.10: Eccentricity.

  * •

Euler Number : The Euler Number returns a value which is the area of the
object minus the number of holes inside.

$E=A-H$ (A.7)

  * •

Equivalent Diameter : This feature measures the equivalent diameter. Equation
A.8 shows how it is computed.

$D=\sqrt{\frac{4A}{\pi}}$ (A.8)

  * •

Perimeter : The perimeter approximates the object to a minimum boundary and
measures the perimeter.

  * •

Maximum Amplitude : The maximum amplitude returns the maximum amplitude value
from the object. The maximum value contains information about the shape,
material and orientation of the object. The material is the most influential
factor, as highly reflective materials have higher returns.

  * •

Mean Amplitude : The mean amplitude averages all the return values from the
object. Getting the mean value can retrieve a better feature since we can
minimize the influence of shape and orientation.

###  A.5 Target Classification

To recognize the objects in the radar image we need to use some classification
algorithm. The Support Vector Machine was chosen to be used since it maximizes
the margin between features creating a better generalization for the
classification.

####  A.5.1 Feature Scaling

Before classifying, feature scaling is necessary to avoid some features having
more influence than others. The rescaling method was used, where the features
are normalized between 0 and 1. Equation A.9 shows how it is computed where x
is one feature vector.

$x^{\prime}=\frac{x-min(x)}{max(x)-min(x)}$ (A.9)

####  A.5.2 Support Vector Machine

The Support Vector Machine (SVM) [222] was used to classify each target. The
SVM has interesting properties as a classifier. It is a linear classifier
which uses support vectors to maximize the margin between the features. The
SVM optimizes the margin between two classes creating a better generalization
of the classification. The SVM used is the C-SVC, it uses a soft-margin
approach, which allows some data inside the margin, and the C is a parameter
that controls it. This important feature helps to create a better
generalization in the classification, and also helps to avoid outliers and
overfitting. The kernel trick is also an important property of the SVM for
non-linear classification - the kernel trick changes the basis of the features
to a higher dimension where it can be linearly separable. The radial basis
function was used to transform our dataset.

###  A.6 Implementation details

The implementation of the methodology was developed using MATLAB 2016b using
LibSVM [223]. The computer used is an Intel i7 3610QM, 8GB RAM. Below is the
time it took for each step of the development:

  * •

The radar imaging procedure takes 2.874 s

  * •

The detection method takes 26.8 ms

  * •

To extract features it takes 41.9 ms

  * •

To train it it takes 41.9 ms

  * •

to test it takes 0.867 ms

The bottleneck of this procedure is the radar imaging process. FFT algorithm
is done for each azimuth to generate the image. But most of the methodology
can be computed in real-time.

###  A.7 Results

This section will show the results for the initial approach developed for this
project. It shows that shape information has a high potential to classify
objects in low-THz radar images.

####  A.7.1 Results on Dataset 1

The dataset 1 is a set with 60 images with 15 images from each class
(pedestrian, trolley, bike and stop sign) was used. Leave-one-out cross
validation was used to evaluate the results. To select the best parameters of
the SVM, grid search was used. Leave-one-out cross validation was used since
we just have 60 samples, and computational power is not an issue for a small
dataset, so we can have a good evaluation of our result with low bias between
the training and the test set.

In Figure A.11 we can visualize the confusion matrix for the target
classification set. As we can see, it generated high accuracy results 91.7%.
Just 5 targets were classified incorrectly.

Figure A.11: Confusion Matrix for the Dataset 1 test set.

Figure A.12 shows some examples of targets with correspondent detection and
its classification, where a: means actual and p: means predicted. Pedestrians
and stop signs have similar shapes, however the material from the stop sign
has a much high reflection, so the amplitude can distinguish between the two
classes.

Figure A.12: Correct recognitions with correspondent detections from the
target classification dataset. a: means actual labels and p: means predicted
labels.

Figure A.13 shows all misclassified targets. The target in Figure A.13c has
some corrupted signals, generating false detections. On features like minor
and major axis this will be much different from the training set, generating
misclassification. Figures A.13a and A.13e contain multi-path returns in the
bottom of the object, making it larger, being misclassified as a bike. Figures
A.13b and A.13d the bike at $90^{o}$ has a higher reflection than in the other
rotations, generating misclassifications.

Figure A.13: Misrecognitions with correspondent detections from the dataset 1.
a: means actual labels and p: means predicted labels.

####  A.7.2 Results on Dataset 2

The dataset 2 is a more challenging scenario. It was not considered as target
localization, the localization used is from the annotation given. The objects
that are not present in the training set (cones, bumps and tiles) were not
considered for the results. Localization and non-objects are addressed in
Chapters 4, 5 and 7.

In the dataset 2, 119 objects were used, 55 of which are pedestrians, 22 are
trolleys, 24 bikes and 18 stop signs. Figure A.14 shows the confusion matrix
for the dataset 2 as test set. The training model was generated using the
dataset 1.

Figure A.14: Confusion Matrix for the Dataset 2.

The accuracy for the dataset 2 is 77.3 %. The accuracy results were 14.5 %
lower than the dataset 1. The dataset 2 is a bit more challenging than the
dataset 1 set. Occlusion between objects occurs in this scenario and some
objects are close to each other. Since the scenarios are not the same, when
the features are extracted, the data points do not correspond to the same
distribution, giving different accuracy results. Some examples of correct
classification can be seen in Figure A.15. The correct examples were similar
to the training examples, classifying the objects correctly.

Figure A.15: Correct recognitions with correspondent detections from the
dataset 2. a: means actual labels and p: means predicted labels.

Figure A.16 shows some examples of misclassification. Figure A.16a and A.16d
are caused by corrupted image acquisition. It is not known why it happened,
but it only happened in very few examples. Figures A.16b and A.16c show the
result when two objects are close to generating misclassification. Figure
A.16e is a misclassification caused by occlusion. The bike in the image is
occluded by other objects, causing its return reflection to be low. The
threshold method to retrieve a good detection was not enough for that case.

Figure A.16: Misrecognitions with correspondent detections from the test set.
a: means actual labels and p: means predicted labels.

###  A.8 Conclusions

A methodology of object recognition on a 150 GHz radar is developed. Since we
had a very small dataset, hand-crafted features based and on shape and
intensity were used to discriminate the objects, opposed to using deep
learning techniques.

The shape features were enough for most of the cases. For objects with similar
features like a stop sign and a pedestrian, the classification can distinguish
between them by their amplitude features, since the stop sign has a high
reflectivity due to its metal material. Objects like a trolley and a bike have
similar amplitude values due to reflective material, but they have different
shapes.

We can list the main problems of the technique based on shape and amplitude:

  * •

Different signatures depending on the object pose and range.

  * •

Difficulty to resolve when two objects are close.

  * •

Corrupted data that is sometimes given by the radar.

  * •

Occluded objects will have low return, making it more difficult to have
accurate detection in that case.

It was decided to add this chapter to the appendix since it was a preliminary
work developed in the beginning of my PhD. This work was used as proof of a
concept that it is possible to recognise objects using high-resolution radar
imaging. In Chapters 4 and 5, we introduced a prototypical higher resolution
radar sensor (300 GHz) and developed an object recognition methodology based
on deep neural networks.

## References

  * [1] Ford, “Ford targets fully autonomous vehicle for ride sharing in 2021.” https://media.ford.com/content/fordmedia/fna/us/en/news/2016/08/16/ford-targets-fully-autonomous-vehicle-for-ride-sharing-in-2021.html.
  * [2] Volvo, “Volvo is about to go through the most ambitious self-driving car experiment ever.” http://uk.businessinsider.com/volvo-to-test-driverless-cars-in-china-2016-4?r=US&IR=T.
  * [3] Nissan, “Nissan’s autonomous drive car.” https://www.nissanusa.com/blog/autonomous-drive-car.
  * [4] BMW, “Bmw will release self-driving cars in china in 2021.” http://uk.businessinsider.com/bmw-releasing-self-driving-cars-in-china-in-2021-2016-8?r=US&IR=T.
  * [5] KAIST, “Korean competition shows weather still a challenge for autonomous cars.” http://spectrum.ieee.org/cars-that-think/transportation/advanced-cars/japan-competition-shows-weather-still-a-challenge-for-autonomous-cars.
  * [6] C. N. Dickson, A. M. Wallace, M. Kitchin, and B. Connor, “Long-wave infrared polarimetric cluster-based vehicle detection,” JOSA A, vol. 32, no. 12, pp. 2307–2315, 2015.
  * [7] C. Dickson, A. Wallace, M. Kitchin, and B. Connor, “Improving infrared vehicle detection with polarisation,” in Proceedings of the IET Intelligent Signal Processing Conference 2013, 12 2013.
  * [8] R. Abbott, N. Robertson, J. Martinez del Rincon, and B. Connor, “Multi-modal object detection using unsupervised transfer learning and adaptation techniques,” in SPIE Security Defence : Artificial Intelligence and Machine Learning in Defense Applications, 5 2019.
  * [9] M. Skolnik et al., “Introduction to Radar Systems,” Mc Graw-Hill, 2001.
  * [10] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” in Advances in neural information processing systems, pp. 91–99, 2015.
  * [11] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European Conference on Computer Vision – ECCV 2014, pp. 740–755, Springer International Publishing, 2014.
  * [12] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems, pp. 1097–1105, 2012.
  * [13] T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2999–3007, Oct 2017.
  * [14] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern recognition, pp. 779–788, 2016.
  * [15] M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (voc) challenge,” International Journal of Computer Vision, vol. 88, pp. 303–338, Jun 2010.
  * [16] D. Jasteh, M. Gashinova, E. Hoare, T.-Y. Tran, N. Clarke, and M. Cherniakov, “Low-thz imaging radar for outdoor applications,” in Radar Symposium (IRS), 2015 16th International, pp. 203–208, IEEE, 2015.
  * [17] D. Jasteh, Experimental low-THz imaging radar for automotive applications.  PhD thesis, The University of Birmingham, 2017.  https://etheses.bham.ac.uk/id/eprint/7523/.
  * [18] S. M. Patole, M. Torlak, D. Wang, and M. Ali, “Automotive radars: A review of signal processing techniques,” IEEE Signal Processing Magazine, vol. 34, no. 2, pp. 22–35, 2017.
  * [19] O. Schumann, M. Hahn, J. Dickmann, and C. Wöhler, “Semantic segmentation on radar point clouds,” in 2018 21st International Conference on Information Fusion (FUSION), pp. 2179–2186, IEEE, 2018.
  * [20] X. Dong, P. Wang, P. Zhang, and L. Liu, “Probabilistic oriented object detection in automotive radar,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2020\.
  * [21] B. Major, D. Fontijne, A. Ansari, R. Teja Sukhavasi, R. Gowaikar, M. Hamilton, S. Lee, S. Grzechnik, and S. Subramanian, “Vehicle detection with automotive radar using deep learning on range-azimuth-doppler tensors,” in The IEEE International Conference on Computer Vision (ICCV) Workshops, pp. 0–0, Oct 2019.
  * [22] L. Sless, B. El Shlomo, G. Cohen, and S. Oron, “Road scene understanding by occupancy grid learning from sparse radar clusters using semantic segmentation,” in The IEEE International Conference on Computer Vision (ICCV) Workshops, Oct 2019.
  * [23] Wikipedia, the free encyclopedia, “Electromagnect spectrum.” https://commons.wikimedia.org/wiki/File:Electromagnetic_spectrum_with_sources.svg.  [Online; accessed April 03, 2020].
  * [24] J. D. Jackson, Classical electrodynamics; 2nd ed. New York, NY: Wiley, 1975.
  * [25] Wikipedia, the free encyclopedia, “Image sensor.” https://en.wikipedia.org/wiki/Image_sensor.  [Online; accessed April 03, 2020].
  * [26] B. Daniel Durini, High Performance Silicon Imaging: Fundamentals and Applications of CMOS and CCD sensors.  Woodhead Publishing Series in Electronic and Optical Materials, Elsevier Science, 2014.
  * [27] Wikipedia, the free encyclopedia, “Lidar.” https://en.wikipedia.org/wiki/Lidar.  [Online; accessed April 03, 2020].
  * [28] M. Pfennigbauer, C. Wolf, J. Weinkopf, and A. Ullrich, “Online waveform processing for demanding target situations,” in Laser Radar Technology and Applications XIX; and Atmospheric Propagation XI, vol. 9080, p. 90800J, International Society for Optics and Photonics, 2014.
  * [29] G. Satat, M. Tancik, and R. Raskar, “Towards photography through realistic fog,” in Computational Photography (ICCP), 2018 IEEE International Conference on, pp. 1–10, IEEE, 2018.
  * [30] E. Hecht, Optics.  Pearson education, Addison-Wesley, 2002.
  * [31] “Short range radar reference design using awr1642.” http://www.ti.com/lit/ug/tidud36b/tidud36b.pdf, April 2017.
  * [32] A. Stove, “Potential applications for low-tera-hertz radar,” in 2015 16th International Radar Symposium (IRS), pp. 191–196, IEEE, 2015.
  * [33] H. Maitre, Processing of Synthetic Aperture Radar (SAR) Images.  Wiley-IEEE Press, 1 ed., 2008.
  * [34] Wikipedia, the free encyclopedia, “Synthetic-aperture radar.” https://en.wikipedia.org/wiki/Synthetic-aperture_radar.  [Online; accessed April 03, 2020].
  * [35] A. F. Yegulalp, “Fast backprojection algorithm for synthetic aperture radar,” in Proceedings of the 1999 IEEE Radar Conference. Radar into the Next Millennium (Cat. No. 99CH36249), pp. 60–65, IEEE, 1999.
  * [36] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986\.
  * [37] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature, vol. 521, no. 7553, pp. 436–444, 2015.
  * [38] W. S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” The bulletin of mathematical biophysics, vol. 5, no. 4, pp. 115–133, 1943.
  * [39] B. Widrow and M. E. Hoff, “Adaptive switching circuits,” tech. rep., Stanford Univ Ca Stanford Electronics Labs, 1960.
  * [40] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” nature, vol. 323, no. 6088, pp. 533–536, 1986\.
  * [41] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning.  The MIT Press, 2016.
  * [42] R. Shanmugamani, Deep Learning for Computer Vision.  Packt Publishing, 2018.
  * [43] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res., vol. 15, pp. 1929–1958, Jan. 2014\.
  * [44] Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in International Conference on Machine Learning, pp. 1050–1059, 2016.
  * [45] SAE, “SAE levels of driving automation.” http://www.sae.org/misc/pdfs/automated_driving.pdf, 2017.  Accessed: 2017-04-17.
  * [46] C. Murray, “Automakers, suppliers ratchet up autonomous car programs.” https://www.designnews.com/automotive-0/automakers-suppliers-ratchet-autonomous-car-programs/189058092646554, 2020\.  Accessed: 2020-04-07.
  * [47] E. D. Dickmanns and A. Zapp, “A curvature-based scheme for improving road vehicle guidance by computer vision,” in Cambridge Symposium Intelligent Robotics Systems, pp. 161–168, International Society for Optics and Photonics, 1987.
  * [48] D. A. Pomerleau, “ALVINN: An autonomous land vehicle in a neural network,” in Advances in neural information processing systems, pp. 305–313, 1989\.
  * [49] J. Malik, “Self driving cars: Is autonomous driving ai-complete.” https://www.youtube.com/watch?v=ltbufoOON8Y, 2017.  Accessed: 2020-04-07.
  * [50] S. Thrun, M. Montemerlo, H. Dahlkamp, D. Stavens, A. Aron, J. Diebel, P. Fong, J. Gale, M. Halpenny, G. Hoffmann, et al., “Stanley: The robot that won the darpa grand challenge,” Journal of field Robotics, vol. 23, no. 9, pp. 661–692, 2006.
  * [51] T. Litman, Autonomous Vehicle Implementation Predictions: Implications for Transport Planning.  desLibris: Documents collection, Victoria Transport Policy Institute, 2013\.
  * [52] A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? The KITTI vision benchmark suite,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354–3361, June 2012.
  * [53] Y. Xiang, W. Choi, Y. Lin, and S. Savarese, “Subcategory-aware convolutional neural networks for object proposals and detection,” arXiv preprint arXiv:1604.04693, 2016.
  * [54] J. Zbontar and Y. LeCun, “Stereo matching by training a convolutional neural network to compare image patches,” Journal of Machine Learning Research, vol. 17, no. 1-32, p. 2, 2016.
  * [55] E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, and T. Brox, “Flownet 2.0: Evolution of optical flow estimation with deep networks,” arXiv preprint arXiv:1612.01925, 2016.
  * [56] M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, et al., “End to end learning for self-driving cars,” arXiv preprint arXiv:1604.07316, 2016.
  * [57] K. Kidono, T. Miyasaka, A. Watanabe, T. Naito, and J. Miura, “Pedestrian recognition using high-definition lidar,” in Intelligent Vehicles Symposium (IV), 2011 IEEE, pp. 405–410, IEEE, 2011.
  * [58] C. Premebida, G. Monteiro, U. Nunes, and P. Peixoto, “A lidar and vision-based approach for pedestrian and vehicle detection and tracking,” in Intelligent Transportation Systems Conference, 2007. ITSC 2007. IEEE, pp. 1044–1049, IEEE, 2007.
  * [59] A. B. Krishnan and J. Kollipara, “Intelligent indoor mobile robot navigation using stereo vision,” arXiv preprint arXiv:1412.6153, 2014.
  * [60] C. Cadena, L. Carlone, H. Carrillo, Y. Latif, D. Scaramuzza, J. Neira, I. Reid, and J. J. Leonard, “Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age,” IEEE Transactions on Robotics, vol. 32, no. 6, pp. 1309–1332, 2016.
  * [61] Y. Wu, Y. Liu, J. Li, H. Liu, and X. Hu, “Traffic sign detection based on convolutional neural networks,” in Neural Networks (IJCNN), The 2013 International Joint Conference on, pp. 1–7, IEEE, 2013.
  * [62] M. Ghafoorian, C. Nugteren, N. Baka, O. Booij, and M. Hofmann, “El-GAN: Embedding loss driven generative adversarial networks for lane detection,” in Proceedings of the European Conference on Computer Vision (ECCV), pp. 0–0, 2018.
  * [63] P. Radecki, M. Campbell, and K. Matzen, “All weather perception: Joint data association, tracking, and classification for autonomous ground vehicles,” arXiv preprint arXiv:1605.02196, 2016.
  * [64] R. O. Chavez-Garcia and O. Aycard, “Multiple sensor fusion and classification for moving object detection and tracking,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 2, pp. 525–534, 2016.
  * [65] H. Cho, Y.-W. Seo, B. V. Kumar, and R. R. Rajkumar, “A multi-sensor fusion system for moving object detection and tracking in urban driving environments,” in Robotics and Automation (ICRA), 2014 IEEE International Conference on, pp. 1836–1843, IEEE, 2014.
  * [66] J. J. DiCarlo, D. Zoccolan, and N. C. Rust, “How does the brain solve visual object recognition?,” Neuron, vol. 73, no. 3, pp. 415–434, 2012.
  * [67] G. Cheng and J. Han, “A survey on object detection in optical remote sensing images,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 117, pp. 11–28, 2016.
  * [68] L. G. Roberts, Machine perception of three-dimensional soups.  PhD thesis, Massachusetts Institute of Technology, 1963.
  * [69] D. G. Lowe, “Three-dimensional object recognition from single two-dimensional images,” Artificial intelligence, vol. 31, no. 3, pp. 355–395, 1987.
  * [70] O. D. Faugeras and M. Hebert, “The representation, recognition, and locating of 3-d objects,” The International Journal of Robotics Research, vol. 5, no. 3, pp. 27–52, 1986.
  * [71] D. P. Huttenlocher and S. Ullman, “Object recognition using alignment,” in Proc. ICCV, vol. 87, pp. 102–111, 1987.
  * [72] P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Transactions on pattern analysis and machine intelligence, vol. 12, no. 7, pp. 629–639, 1990.
  * [73] T. Leung and J. Malik, “Representing and recognizing the visual appearance of materials using three-dimensional textons,” International Journal of Computer Vision, vol. 43, no. 1, pp. 29–44, 2001.
  * [74] C. Schmid and R. Mohr, “Local grayvalue invariants for image retrieval,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 5, pp. 530–535, 1997.
  * [75] P. Viola and M. Jones, “Robust real-time object detection,” International Journal of Computer Vision, no. 4, pp. 34–47, 2001.
  * [76] S. Belongie, J. Malik, and J. Puzicha, “Shape matching and object recognition using shape contexts,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 4, pp. 509–522, 2002.
  * [77] D. G. Lowe, “Object recognition from local scale-invariant features,” in Computer vision, 1999. The proceedings of the seventh IEEE international conference on, vol. 2, pp. 1150–1157, Ieee, 1999.
  * [78] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Computer Vision and Pattern Recognition, 2005. CVPR 2005\. IEEE Computer Society Conference on, vol. 1, pp. 886–893, IEEE, 2005.
  * [79] P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1627–1645, 2010.
  * [80] Y. Freund and R. Schapire, “A short introduction to boosting,” Journal-Japanese Society For Artificial Intelligence, vol. 14, no. 771-780, p. 1612, 1999.
  * [81] C. Cortes and V. Vapnik, “Support-vector networks,” Machine learning, vol. 20, no. 3, pp. 273–297, 1995.
  * [82] L. Breiman, “Random forests,” Machine learning, vol. 45, no. 1, pp. 5–32, 2001.
  * [83] G. Csurka, C. Dance, L. Fan, J. Willamowski, and C. Bray, “Visual categorization with bags of keypoints,” in Workshop on statistical learning in computer vision, ECCV, 2004.
  * [84] J. Deng, W. Dong, R. Socher, L. Li, Kai Li, and Li Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255, June 2009\.
  * [85] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.
  * [86] C. Szegedy, Wei Liu, Yangqing Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9, June 2015.
  * [87] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778, 2016.
  * [88] S. Hochreiter, Y. Bengio, P. Frasconi, and J. Schmidhuber, “Gradient flow in recurrent nets: the difficulty of learning long-term dependencies.” https://ml.jku.at/publications/older/ch7.pdf.
  * [89] P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. Lecun, “Overfeat: Integrated recognition, localization and detection using convolutional networks,” in International Conference on Learning Representations (ICLR2014), CBLS, April 2014, 2014.
  * [90] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern recognition, pp. 580–587, June 2014.
  * [91] R. Girshick, “Fast R-CNN,” in Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448, Dec 2015.
  * [92] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, pp. 1137–1149, June 2017.
  * [93] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Fu, and A. Berg, “SSD: Single shot multibox detector,” in Computer Vision – ECCV 2016 (B. Leibe, J. Matas, N. Sebe, and M. Welling, eds.), (Cham), pp. 21–37, Springer International Publishing, 2016.
  * [94] J. R. Uijlings, K. E. Van De Sande, T. Gevers, and A. W. Smeulders, “Selective search for object recognition,” International Journal of Computer Vision, vol. 104, no. 2, pp. 154–171, 2013.
  * [95] Z. Tian, C. Shen, H. Chen, and T. He, “FCOS: Fully convolutional one-stage object detection,” in The IEEE International Conference on Computer Vision (ICCV), October 2019.
  * [96] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The KITTI dataset,” The International Journal of Robotics Research, vol. 32, no. 11, pp. 1231–1237, 2013.
  * [97] T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu, “An efficient k-means clustering algorithm: Analysis and implementation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 881–892, 2002.
  * [98] M. Ester, H. Kriegel, J. Sander, and X. Xiaowei, “A density-based algorithm for discovering clusters in large spatial databases with noise,” tech. rep., AAAI Press, Menlo Park, CA (United States), 1996.
  * [99] J. Shi and J. Malik, “Normalized cuts and image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888–905, 2000.
  * [100] V. Kolmogorov and R. Zabin, “What energy functions can be minimized via graph cuts?,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 2, pp. 147–159, 2004.
  * [101] H. Isack and Y. Boykov, “Energy-based geometric multi-model fitting,” International Journal of Computer Vision, vol. 97, no. 2, pp. 123–147, 2012\.
  * [102] D. Z. Wang, I. Posner, and P. Newman, “What could move? finding cars, pedestrians and bicyclists in 3d laser data,” in Robotics and Automation (ICRA), 2012 IEEE International Conference on, (Minnesota, USA), pp. 4038–4044, IEEE, May 2012.
  * [103] A. E. Johnson, Spin-images: a representation for 3-D surface matching.  PhD thesis, Carnegie Mellon University, 1997.  https://www.ri.cmu.edu/publications/spin-images-a-representation-for-3-d-surface-matching/.
  * [104] A. E. Johnson and M. Hebert, “Using spin images for efficient object recognition in cluttered 3d scenes,” IEEE Transactions on pattern analysis and machine intelligence, vol. 21, no. 5, pp. 433–449, 1999.
  * [105] R. Osada, T. Funkhouser, B. Chazelle, and D. Dobkin, “Shape distributions,” ACM Transactions on Graphics (TOG), vol. 21, no. 4, pp. 807–832, 2002.
  * [106] M. Engelcke, D. Rao, D. Z. Wang, C. H. Tong, and I. Posner, “Vote3deep: Fast object detection in 3d point clouds using efficient convolutional neural networks,” arXiv preprint arXiv:1609.06666, 2016.
  * [107] B. Li, T. Zhang, and T. Xia, “Vehicle detection from 3d lidar using fully convolutional network,” arXiv preprint arXiv:1608.07916, 2016.
  * [108] C. He, H. Zeng, J. Huang, X.-S. Hua, and L. Zhang, “Structure aware single-stage 3d object detection from point cloud,” in Proceedings of the IEEE Conference on Computer Vision and Pattern recognition, 2020.
  * [109] C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “PointNet: Deep learning on point sets for 3d classification and segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern recognition, pp. 652–660, 2017\.
  * [110] C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “PointNet++: Deep hierarchical feature learning on point sets in a metric space,” in Advances in neural information processing systems, pp. 5099–5108, 2017.
  * [111] S. Shi, X. Wang, and H. Li, “PointRCNN: 3d object proposal generation and detection from point cloud,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–779, 2019.
  * [112] FLIR, “Free flir thermal dataset for algorithm training.” https://www.flir.co.uk/oem/adas/adas-dataset-form/.
  * [113] J. W. Davis and M. A. Keck, “A two-stage template approach to person detection in thermal imagery,” in 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION’05) - Volume 1, vol. 1, pp. 364–369, 2005.
  * [114] J. Baek, S. Hong, J. Kim, and E. Kim, “Efficient pedestrian detection at nighttime using a thermal camera,” Sensors, vol. 17, no. 8, p. 1850, 2017\.
  * [115] D. Zeng and M. Zhu, “Multiscale fully convolutional network for foreground object detection in infrared videos,” IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 4, pp. 617–621, 2018.
  * [116] R. Abbott, J. M. Del Rincon, B. Connor, and N. Robertson, “Deep object classification in low resolution LWIR imagery via transfer learning,” in Proceedings of 5th IMA Conference on Mathematics in Defence., 2017.
  * [117] P. Tait, Introduction to radar target recognition, vol. 18.  IET, 2005.
  * [118] A. Angelov, A. Robertson, R. Murray-Smith, and F. Fioranelli, “Practical classification of different moving targets using automotive radar and deep neural networks,” IET Radar, Sonar Navigation, vol. 12, no. 10, pp. 1082–1089, 2018.
  * [119] F. Fioranelli, M. Ritchie, and H. Griffiths, “Classification of unarmed/armed personnel using the netrad multistatic radar for micro-doppler and singular value decomposition features,” IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 9, pp. 1933–1937, 2015.
  * [120] F. Fioranelli, M. Ritchie, and H. Griffiths, “Multistatic human micro-doppler classification of armed/unarmed personnel,” IET Radar, Sonar & Navigation, vol. 9, no. 7, pp. 857–865, 2015.
  * [121] I. Bilik, J. Tabrikian, and A. Cohen, “Gmm-based target classification for ground surveillance doppler radar,” IEEE Transactions on Aerospace and Electronic Systems, vol. 42, no. 1, pp. 267–278, 2006.
  * [122] K.-T. Kim, D.-K. Seo, and H.-T. Kim, “Efficient radar target recognition using the music algorithm and invariant features,” IEEE Transactions on Antennas and Propagation, vol. 50, no. 3, pp. 325–337, 2002.
  * [123] Z. Wei, W. Jie, and G. Jian, “An efficient sar target recognition algorithm based on contour and shape context,” in Synthetic Aperture Radar (APSAR), 2011 3rd International Asia-Pacific Conference on, pp. 1–4, IEEE, 2011\.
  * [124] M. Saidi, B. Hoeltzener, A. Toumi, A. Khecnhaf, and D. Aboutajdine, “Automatic recognition of isar images: Target shapes features extraction,” in Information and Communication Technologies: From Theory to Applications, 2008\. ICTTA 2008. 3rd International Conference on, pp. 1–6, IEEE, 2008.
  * [125] J.-I. Park, S.-H. Park, and K.-T. Kim, “New discrimination features for sar automatic target recognition,” IEEE Geoscience and Remote Sensing Letters, vol. 10, no. 3, pp. 476–480, 2013.
  * [126] T. D. Ross, S. W. Worrell, V. J. Velten, J. C. Mossing, and M. L. Bryant, “Standard sar atr evaluation experiments using the mstar public release data set,” in Aerospace/Defense Sensing and Controls, pp. 566–573, International Society for Optics and Photonics, 1998.
  * [127] J.-I. Park and K.-T. Kim, “Modified polar mapping classifier for sar automatic target recognition,” IEEE Transactions on Aerospace and Electronic Systems, vol. 50, no. 2, pp. 1092–1107, 2014.
  * [128] S. Chen, H. Wang, F. Xu, and Y. Jin, “Target classification using the deep convolutional networks for sar images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, pp. 4806–4817, Aug 2016.
  * [129] D. M. Grimes and T. O. Jones, “Automotive radar: A brief review,” Proceedings of the IEEE, vol. 62, no. 6, pp. 804–822, 1974.
  * [130] P. Ganci, S. Potts, and F. Okurowski, “A forward looking automotive radar sensor,” in Intelligent Vehicles’ 95 Symposium., Proceedings of the, pp. 321–325, IEEE, 1995.
  * [131] R. H. Rasshofer, “Functional requirements of future automotive radar systems,” in Radar Conference, 2007. EuRAD 2007. European, pp. 259–262, IEEE, 2007.
  * [132] H. Rohling, S. Heuel, and H. Ritter, “Pedestrian detection procedure integrated into an 24 ghz automotive radar,” in Radar Conference, 2010 IEEE, pp. 1229–1232, IEEE, 2010.
  * [133] A. Bartsch, F. Fitzek, and R. Rasshofer, “Pedestrian recognition using automotive radar sensors,” Advances in Radio Science: ARS, vol. 10, 2012\.
  * [134] V. Nordenmark and A. Forsgren, “Radar-detection based classification of moving objects using machine learning methods,” Master’s thesis, KTH Royal Institute of Technology in Stockholm, 2015.
  * [135] G. Hakobyan and B. Yang, “High-performance automotive radar: A review of signal processing algorithms and modulation schemes,” IEEE Signal Processing Magazine, vol. 36, no. 5, pp. 32–44, 2019.
  * [136] F. Roos, J. Bechter, C. Knill, B. Schweizer, and C. Waldschmidt, “Radar sensors for autonomous driving: Modulation schemes and interference mitigation,” IEEE Microwave Magazine, vol. 20, no. 9, pp. 58–72, 2019\.
  * [137] F. Baselice, G. Ferraioli, S. Lukin, G. Matuozzo, V. Pascazio, and G. Schirinzi, “A new methodology for 3d target detection in automotive radar applications,” Sensors, vol. 16, no. 5, p. 614, 2016.
  * [138] A. Palffy, J. Dong, J. F. P. Kooij, and D. M. Gavrila, “Cnn based road user detection using the 3d radar cube,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 1262–1269, 2020.
  * [139] Velodyne, “Velodyne lidar product guide.” https://velodynelidar.com/products/hdl-64e/, 2020.  Accessed: 2020-09-13.
  * [140] T. Instruments, “Texas instruments awr1443.” https://www.ti.com/product/AWR1443, 2020.
  * [141] Navtech, “Navtech radar technical specifications.” https://navtechradar.com/clearway-technical-specifications/.  [Online; accessed April 03, 2020].
  * [142] L. Daniel, D. Phippen, E. Hoare, A. Stove, M. Cherniakov, and M. Gashinova, “Low-thz radar, lidar and optical imaging through artificially generated fog,” in International Conference on Radar Systems (Radar 2017), pp. 1–4, IET, 2017.
  * [143] S. Labs, “Zed stereo camera.” https://www.stereolabs.com/zed/, 2020.
  * [144] M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
  * [145] G. Lin, G. Fan, L. Yu, X. Kang, and E. Zhang, “Heterogeneous structure fusion for target recognition in infrared imagery,” in Proceeding of CVPR Workshop, vol. 8160, pp. 118–125, 2015.
  * [146] M. Khan, G. Fan, D. Heisterkamp, and L. Yu, “Automatic target recognition in infrared imagery using dense hog features and relevance grouping of vocabulary,” in Proceedings of CVPR Workshop, pp. 293–298, 2014.
  * [147] J.-Y. Kwak, B. Ko, and J. Nam, “Pedestrian tracking using online boosted random ferns learning in far-infrared imagery for safe driving at night,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 1, pp. 69–81, 2017.
  * [148] G. Wong, C. McMaster, R. Struthers, A. Gorman, P. Sinclair, R. Lamb, and A. R. Harvey, “A novel snapshot polarimetric imager,” in Electro-Optical and Infrared Systems: Technology and Applications IX, vol. 8541, p. 85410G, International Society for Optics and Photonics, 2012.
  * [149] D. A. Lavigne, M. Breton, G. Fournier, J.-F. Charette, M. Pichette, V. Rivet, and A.-P. Bernier, “Target discrimination of man-made objects using passive polarimetric signatures acquired in the visible and infrared spectral bands,” in Polarization Science and Remote Sensing V, vol. 8160, p. 816007, International Society for Optics and Photonics, 2011.
  * [150] B. Connor, J. Letham, N. Robertson, and I. Carrie, “Scene understanding and task optimisation using multimodal imaging sensors and context: a real-time implementation,” in Infrared Technology and Applications XXXVII, vol. 8012, p. 80120A, International Society for Optics and Photonics, 2011.
  * [151] B. D. Bartlett, A. Schlamm, C. Salvaggio, and D. W. Messinger, “Anomaly detection of man-made objects using spectropolarimetric imagery,” in Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, vol. 8048, p. 80480B, International Society for Optics and Photonics, 2011.
  * [152] J. M. Romano, D. Rosario, and J. McCarthy, “Day/night polarimetric anomaly detection using spice imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 12, pp. 5014–5023, 2012.
  * [153] I. Rodger, B. Connor, and N. Robertson, “Classifying objects in lwir imagery via cnns,” in Proc. SPIE: Electro-Optical and Infrared Systems: Technology and Applications XIII, 2016.
  * [154] J. Liu, S. Zhang, S. Wang, and D. N. Metaxas, “Multispectral deep neural networks for pedestrian detection,” arXiv preprint arXiv:1611.02644, 2016\.
  * [155] S. Hwang, J. Park, N. Kim, Y. Choi, and I. S. Kweon, “Multispectral pedestrian detection: Benchmark dataset and baseline,” in Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on, pp. 1037–1045, IEEE, 2015\.
  * [156] E. Gundogdu, A. Koc, and A. AA, “Automatic target recognition and detection in infrared imagery under cluttered background,” in Proceedings of SPIE Conferenec on Security and Defence, vol. 10432, Target and Background Signatures, 2017.
  * [157] R. Azzam and N. Bashara, Ellipsometry and polarized light.  North-Holland personal library, North-Holland Pub. Co., 1977.
  * [158] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” CoRR, vol. abs/1409.1556, 2014.
  * [159] A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint arXiv:1704.04861, 2017.
  * [160] M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, et al., “Tensorflow: A system for large-scale machine learning.,” in OSDI, vol. 16, pp. 265–283, 2016.
  * [161] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European Conference on Computer Vision, pp. 740–755, Springer, 2014.
  * [162] M. Everingham, S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes challenge: A retrospective,” International Journal of Computer Vision, vol. 111, no. 1, pp. 98–136, 2015.
  * [163] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in International Conference on Learning Representations, 2015.
  * [164] L. Daniel, A. Stove, E. Haore, D. Phippen, M. Cherniakov, M. Mulgrew, and M. Gashinova, “Application of doppler beam sharpening for azimuth refinemnet in prospective low-thz automotive radars,” IET Radar, Sonar and Navigation, vol. 12, no. 10, pp. 1121–1130, 2018.
  * [165] Y. LeCun and Y. Bengio, “Convolutional networks for images, speech, and time series,” in The Handbook of Brain Theory and Neural Networks (M. A. Arbib, ed.), pp. 255–258, Cambridge, MA, USA: MIT Press, 1998.
  * [166] X. Huang, M. Kwiatkowska, S. Wang, and M. Wu, “Safety verification of deep neural networks,” in International Conference on Computer Aided Verification, pp. 3–29, Springer, 2017.
  * [167] A. Nguyen, J. Yosinski, and J. Clune, “Deep neural networks are easily fooled: High confidence predictions for unrecognizable images,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 427–436, June 2015.
  * [168] D. Phippen, L. Daniel, M. Gashinova, and A. Stove, “Trilateralisation of targets using a 300 ghz radar system,” in International Conference on Radar Systems, IEEE, 2017.
  * [169] X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp. 249–256, 2010.
  * [170] J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?,” in Advances in Neural Information Processing Systems, pp. 3320–3328, 2014.
  * [171] E. Keydel, S. Lee, and J. Moore, “Mstar extended operating conditions: A tutorial,” in Algorithms for Synthetic Aperture Radar Imagery III, vol. 2757, pp. 228–242, International Society for Optics and Photonics, 1996\.
  * [172] L. Van der Maaten and G. Hinton, “Visualizing data using t-sne,” Journal of Machine Learning Research, vol. 9, no. Nov, pp. 2579–2605, 2008.
  * [173] M. Richards, Fundamentals Of Radar Signal Processing.  McGraw-Hill Education (India) Pvt Limited, 2005.
  * [174] P. Dollar, C. Wojek, B. Schiele, and P. Perona, “Pedestrian detection: An evaluation of the state of the art,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 4, pp. 743–761, 2012.
  * [175] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788, June 2016.
  * [176] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise,” in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD’96, pp. 226–231, AAAI Press, 1996.
  * [177] E. Marchetti, R. Du, B. Willetts, F. Norouzian, E. G. Hoare, T. Y. Tran, N. Clarke, M. Cherniakov, and M. Gashinova, “Radar cross-section of pedestrians in the low-thz band,” IET Radar, Sonar Navigation, vol. 12, no. 10, pp. 1104–1113, 2018.
  * [178] J. Lombacher, K. Laudt, M. Hahn, J. Dickmann, and C. Wöhler, “Semantic radar grids,” in 2017 IEEE Intelligent Vehicles Symposium (IV), pp. 1170–1175, June 2017.
  * [179] “Radar tutorial.” https://www.radartutorial.eu/index.en.html, 2019.  Accessed: 2019-10-14.
  * [180] E. F. Knott, J. F. Schaeffer, and M. T. Tulley, Radar cross section.  SciTech Publishing, 2004.
  * [181] K. Schuler, D. Becker, and W. Wiesback, “Extraction of virtual scattering centers of vehicles by ray-tracing simulations,” IEEE Transactions on Antennas ans Propagation, vol. 56, no. 11, pp. 3543–3551, 2008.
  * [182] J. Ding, B. Chen, H. Liu, and M. Huang, “Convolutional neural network with data augmentation for sar target recognition,” IEEE Geoscience and remote sensing letters, vol. 13, no. 3, pp. 364–368, 2016.
  * [183] A. B. Jung, K. Wada, J. Crall, S. Tanaka, J. Graving, S. Yadav, J. Banerjee, G. Vecsei, A. Kraft, J. Borovec, C. Vallentin, S. Zhydenko, K. Pfeiffer, B. Cook, I. Fernández, W. Chi-Hung, A. Ayala-Acevedo, R. Meudec, M. Laporte, et al., “imgaug.” https://github.com/aleju/imgaug, 2019.  Online; accessed 25-Sept-2019.
  * [184] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The KITTI dataset,” International Journal of Robotics Research (IJRR), 2013\.
  * [185] H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” arXiv preprint arXiv:1903.11027, 2019.
  * [186] P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine, et al., “Scalability in perception for autonomous driving: An open dataset benchmark,” arXiv preprint arXiv:1912.04838, 2019.
  * [187] C. Sakaridis, D. Dai, and L. Van Gool, “Semantic foggy scene understanding with synthetic data,” International Journal of Computer Vision, vol. 126, pp. 973–992, Sep 2018.
  * [188] M. Bijelic, T. Gruber, and W. Ritter, “A benchmark for lidar sensors in fog: Is detection breaking down?,” in 2018 IEEE Intelligent Vehicles Symposium (IV), pp. 760–767, IEEE, 2018.
  * [189] M. Kutila, P. Pyykönen, H. Holzhüter, M. Colomb, and P. Duthon, “Automotive lidar performance verification in fog and rain,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 1695–1701, IEEE, 2018.
  * [190] A. M. Wallace, A. Halimi, and G. S. Buller, “Full waveform lidar for adverse weather conditions,” IEEE Transactions on Vehicular Technology, vol. Early Access, 2020.
  * [191] F. Norouzian, E. Marchetti, E. Hoare, M. Gashinova, C. Constantinou, P. Gardner, and M. Cherniakov, “Experimental study on low-thz automotive radar signal attenuation during snowfall,” IET Radar, Sonar & Navigation, vol. 13, no. 9, pp. 1421–1427, 2019.
  * [192] F. Norouzian, E. Marchetti, M. Gashinova, E. Hoare, C. Constantinou, P. Gardner, and M. Cherniakov, “Rain attenuation at millimeter wave and low-thz frequencies,” IEEE Transactions on Antennas and Propagation, vol. 68, no. 1, pp. 421–431, 2019.
  * [193] M.-F. Chang, J. Lambert, P. Sangkloy, J. Singh, S. Bak, A. Hartnett, D. Wang, P. Carr, S. Lucey, D. Ramanan, and J. Hays, “Argoverse: 3d tracking and forecasting with rich maps,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.
  * [194] D. Barnes, M. Gadd, P. Murcutt, P. Newman, and I. Posner, “The oxford radar robotcar dataset: A radar extension to the oxford robotcar dataset,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), (Paris), 2020.
  * [195] G. Kim, Y. S. Park, Y. Cho, J. Jeong, and A. Kim, “Mulran: Multimodal range dataset for urban place recognition,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), (Paris), May 2020\.  Accepted. To appear.
  * [196] M. Meyer and G. Kuschk, “Automotive radar dataset for deep learning based 3d object detection,” in 2019 16th European Radar Conference (EuRAD), pp. 129–132, IEEE, 2019.
  * [197] Y. Wang, Z. Jiang, X. Gao, J.-N. Hwang, G. Xing, and H. Liu, “Rodnet: Object detection under severe conditions using vision-radio cross-modal supervision,” arXiv preprint arXiv:2003.01816, 2020.
  * [198] M. Quigley, K. Conley, B. P. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, and A. Y. Ng, “Ros: an open-source robot operating system,” in ICRA Workshop on Open Source Software, 2009.
  * [199] Velodyne, “Velodyne hdl-32e.” https://velodynelidar.com/products/hdl-32e/, 2020.
  * [200] “Matlab stereo camera calibration.” https://uk.mathworks.com/help/vision/ref/stereocameracalibrator-app.html, 2020\.  [Online; accessed April 03, 2020].
  * [201] “Cvat: Powerful and efficient computer vision annotation tool.” https://github.com/opencv/cvat.
  * [202] C. Vondrick, D. Patterson, and D. Ramanan, “Efficiently scaling up crowdsourced video annotation,” International Journal of Computer Vision, pp. 1–21, 2013.
  * [203] “Labelimg.” https://github.com/tzutalin/labelImg.  [Online; accessed April 03, 2020].
  * [204] J. G. Allen, R. Y. Xu, J. S. Jin, et al., “Object tracking using camshift algorithm and multiple quantized feature spaces,” in ACM International Conference Proceeding Series, vol. 100, pp. 3–7, 2004.
  * [205] G.-S. Xia, X. Bai, J. Ding, Z. Zhu, S. Belongie, J. Luo, M. Datcu, M. Pelillo, and L. Zhang, “Dota: A large-scale dataset for object detection in aerial images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3974–3983, 2018.
  * [206] B.-N. Vo and W.-K. Ma, “The gaussian mixture probability hypothesis density filter,” IEEE Transactions on signal processing, vol. 54, no. 11, pp. 4091–4104, 2006.
  * [207] A. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft, “Simple online and realtime tracking,” in 2016 IEEE International Conference on Image Processing (ICIP), pp. 3464–3468, IEEE, 2016.
  * [208] N. Wojke, A. Bewley, and D. Paulus, “Simple online and realtime tracking with a deep association metric,” in 2017 IEEE International Conference on Image Processing (ICIP), pp. 3645–3649, 2017.
  * [209] C. Trabelsi, O. Bilaniuk, Y. Zhang, D. Serdyuk, S. Subramanian, J. F. Santos, S. Mehri, N. Rostamzadeh, Y. Bengio, and C. J. Pal, “Deep complex networks,” in International Conference on Learning Representations, 2018\.
  * [210] C. J. Gaudet and A. S. Maida, “Deep quaternion networks,” in 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8, IEEE, 2018\.
  * [211] X. Zhu, Y. Xu, H. Xu, and C. Chen, “Quaternion convolutional neural networks,” in Proceedings of the European Conference on Computer Vision (ECCV), pp. 631–647, 2018.
  * [212] B. Yang, M. Liang, and R. Urtasun, “Hdnet: Exploiting hd maps for 3d object detection,” in Proceedings of The 2nd Conference on Robot Learning (A. Billard, A. Dragan, J. Peters, and J. Morimoto, eds.), vol. 87 of Proceedings of Machine Learning Research, pp. 146–155, PMLR, 29–31 Oct 2018\.
  * [213] M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst, “Geometric deep learning: going beyond euclidean data,” IEEE Signal Processing Magazine, vol. 34, no. 4, pp. 18–42, 2017.
  * [214] M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. R. Salakhutdinov, and A. J. Smola, “Deep sets,” in Advances in neural information processing systems, pp. 3391–3401, 2017.
  * [215] E. Tzeng, J. Hoffman, N. Zhang, K. Saenko, and T. Darrell, “Deep domain confusion: Maximizing for domain invariance,” arXiv preprint arXiv:1412.3474, 2014.
  * [216] Velodyne, “Velodyne lidar product guide.” http://velodynelidar.com/docs/datasheet/LiDAR%20Comparison%20chart_Rev-A_Web.pdf, 2017\.  Accessed: 2017-04-13.
  * [217] K. Muhlmann, D. Maier, R. Hesser, and R. Manner, “Calculating dense disparity maps from color stereo images, an efficient implementation,” in Stereo and Multi-Baseline Vision, 2001.(SMBV 2001). Proceedings. IEEE Workshop on, pp. 30–36, IEEE, 2001.
  * [218] D. Phippen, L. Daniel, E. Hoare, M. Gashinova, and M. Cherniakov, “3d images of pedestrians at 300ghz,” in 2019 20th International Radar Symposium (IRS), pp. 1–10, June 2019.
  * [219] D. R. Vizard, M. Gashinova, E. Hoare, D. Jasteh, L. Daniel, M. Cherniakov, T. Tran, and N. Clarke, “Antenna range evaluations of low thz imagers for automotive applications,” in Antenna Measurements & Applications (CAMA), 2014 IEEE Conference on, pp. 1–4, IEEE, 2014.
  * [220] H. Sun, Z. Liu, and Q. Lin, “Radar target recognition based on micro-doppler effect,” in Signal Processing, 2006 8th International Conference on, vol. 3, IEEE, 2008.
  * [221] S. Heuel and H. Rohling, “Pedestrian recognition in automotive radar sensors,” in Radar Symposium (IRS), 2013 14th International, vol. 2, pp. 732–739, IEEE, 2013.
  * [222] S. R. Gunn et al., “Support vector machines for classification and regression,” ISIS technical report, vol. 14, pp. 85–86, 1998.
  * [223] C.-C. Chang and C.-J. Lin, “Libsvm: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 2, no. 3, p. 27, 2011.