| --- |
| 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 [murad.mebrahtu@ku.ac.ae](murad.mebrahtu@ku.ac.ae) or [https://huggingface.co/Murdism](https://huggingface.co/Murdism) |
