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license: cc-by-nc-sa-4.0
EMT Dataset
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 β Multi-agent interactions in congested environments
- Weather Variations β Clear and rainy conditions
- Visual Challenges β High reflections and adverse weather combinations (e.g., rainy nights)
Dataset Annotations
This dataset provides annotations for:
- Detection & Tracking β 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 β The frame image
- Object β 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.
Our paper can be found Here
For any inquires contact [email protected] or https://huggingface.co/Murdism
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