metadata
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:
- Image – The frame image
- 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.