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---
license: cc-by-nc-sa-4.0
task_categories:
- object-detection
tags:
- object_detection
- Object_tracking
- autonomous_driving
---
---
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](https://huggingface.co/papers/2502.19260).
## 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](https://github.com/AV-Lab/emt-dataset).
---
## Quick Start
```python
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
```python
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](https://github.com/AV-Lab/emt-dataset).
Our paper can be found [Here](https://huggingface.co/papers/2502.19260.)
For any inquires contact [[email protected]]([email protected]) or [https://huggingface.co/Murdism](https://huggingface.co/Murdism) |