| | --- |
| | 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) |
