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license: cc-by-nc-sa-4.0

EMT Dataset

This dataset was presented in EMT: A Visual Multi-Task Benchmark Dataset for Autonomous Driving in the Arab Gulf Region.

Introduction

EMT is a comprehensive dataset for autonomous driving research, containing 57 minutes of diverse urban traffic footage from the Gulf Region. It includes rich semantic annotations across two agent categories:

• People: Pedestrians and cyclists
• Vehicles: Seven different classes

Each video segment spans 2.5-3 minutes, capturing challenging real-world scenarios:

• Dense Urban Traffic – Multi-agent interactions in congested environments
• Weather Variations – Clear and rainy conditions
• Visual Challenges – High reflections and adverse weather combinations (e.g., rainy nights)

Dataset Annotations

This dataset provides annotations for:

• Detection & Tracking – Multi-object tracking with consistent IDs

For intention prediction and trajectory prediction annotations, please refer to our GitHub repository.

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Quick Start

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("KuAvLab/EMT", split="train")

Available Labels

Each dataset sample contains two main components:

1. Image – The frame image
2. Object – The annotations for detected objects

Object Labels

• bbox: Bounding box coordinates (x_min, y_min, x_max, y_max)
• track_id: Tracking ID of detected objects
• class_id: Numeric class ID
• class_name: Object type (e.g., car, pedestrian)

Sample Usage

import numpy as np

for data in dataset:
    # Convert image from PIL to OpenCV format (BGR)
    img = np.array(data['image'])
    
    print("Classes:", data['objects']['class_name'])
    print("Bboxes:",  data['objects']['bbox'])
    print("Track IDs:", data['objects']['track_id'])
    print("Class IDs:", data['objects']['class_id'])

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Data Collection

Aspect	Description
Duration	57 minutes total footage
Segments	2.5-3 minutes per recording
FPS	10 fps for annotated frames
Agent Classes	2 Person categories, 7 Vehicle categories

Agent Categories

People

• Pedestrians
• Cyclists

Vehicles

• Motorbike
• Small motorized vehicle
• Medium vehicle
• Large vehicle
• Car
• Bus
• Emergency vehicle

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Dataset Statistics

Category	Count
Annotated Frames	34,386
Bounding Boxes	626,634
Unique Agents	9,094
Vehicle Instances	7,857
Pedestrian Instances	568

Class Breakdown

Class	Description	Bounding Boxes	Unique Agents
Pedestrian	Walking individuals	24,574	568
Cyclist	Bicycle/e-bike riders	594	14
Motorbike	Motorcycles, bikes, scooters	11,294	159
Car	Standard automobiles	429,705	6,559
Small motorized vehicle	Mobility scooters, quad bikes	767	13
Medium vehicle	Vans, tractors	51,257	741
Large vehicle	Lorries, trucks (6+ wheels)	37,757	579
Bus	School buses, single/double-deckers	19,244	200
Emergency vehicle	Ambulances, police cars, fire trucks	1,182	9
Overall		576,374	8,842

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For more details , visit our GitHub repository.
Our paper can be found Here
For any inquires contact [email protected] or https://huggingface.co/Murdism