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license: cc-by-nc-sa-4.0

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.


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'])

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

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

For more details , visit our GitHub repository.