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license: cc-by-nc-sa-4.0 |
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task_categories: |
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- object-detection |
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tags: |
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- object_detection |
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- Object_tracking |
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- autonomous_driving |
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license: cc-by-nc-sa-4.0 |
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--- |
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# EMT Dataset |
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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). |
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## Introduction |
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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: |
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- **People**: Pedestrians and cyclists |
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- **Vehicles**: Seven different classes |
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Each video segment spans **2.5-3 minutes**, capturing challenging real-world scenarios: |
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- **Dense Urban Traffic** – Multi-agent interactions in congested environments |
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- **Weather Variations** – Clear and rainy conditions |
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- **Visual Challenges** – High reflections and adverse weather combinations (e.g., rainy nights) |
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### Dataset Annotations |
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This dataset provides annotations for: |
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- **Detection & Tracking** – Multi-object tracking with consistent IDs |
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For **intention prediction** and **trajectory prediction** annotations, please refer to our [GitHub repository](https://github.com/AV-Lab/emt-dataset). |
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## Quick Start |
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```python |
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from datasets import load_dataset |
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# Load the dataset |
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dataset = load_dataset("KuAvLab/EMT", split="train") |
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``` |
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### Available Labels |
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Each dataset sample contains two main components: |
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1. **Image** – The frame image |
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2. **Object** – The annotations for detected objects |
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#### Object Labels |
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- **bbox**: Bounding box coordinates (`x_min, y_min, x_max, y_max`) |
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- **track_id**: Tracking ID of detected objects |
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- **class_id**: Numeric class ID |
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- **class_name**: Object type (e.g., `car`, `pedestrian`) |
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#### Sample Usage |
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```python |
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import numpy as np |
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for data in dataset: |
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# Convert image from PIL to OpenCV format (BGR) |
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img = np.array(data['image']) |
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print("Classes:", data['objects']['class_name']) |
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print("Bboxes:", data['objects']['bbox']) |
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print("Track IDs:", data['objects']['track_id']) |
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print("Class IDs:", data['objects']['class_id']) |
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``` |
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## Data Collection |
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| Aspect | Description | |
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|------------|----------------------------------| |
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| Duration | 57 minutes total footage | |
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| Segments | 2.5-3 minutes per recording | |
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| FPS | 10 fps for annotated frames | |
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| Agent Classes | 2 Person categories, 7 Vehicle categories | |
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### Agent Categories |
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#### **People** |
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- Pedestrians |
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- Cyclists |
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#### **Vehicles** |
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- Motorbike |
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- Small motorized vehicle |
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- Medium vehicle |
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- Large vehicle |
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- Car |
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- Bus |
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- Emergency vehicle |
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## Dataset Statistics |
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| Category | Count | |
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|-------------------|------------| |
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| Annotated Frames | 34,386 | |
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| Bounding Boxes | 626,634 | |
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| Unique Agents | 9,094 | |
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| Vehicle Instances | 7,857 | |
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| Pedestrian Instances | 568 | |
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### Class Breakdown |
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| **Class** | **Description** | **Bounding Boxes** | **Unique Agents** | |
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|---------------------------|----------------|-------------------|----------------| |
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| Pedestrian | Walking individuals | 24,574 | 568 | |
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| Cyclist | Bicycle/e-bike riders | 594 | 14 | |
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| Motorbike | Motorcycles, bikes, scooters | 11,294 | 159 | |
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| Car | Standard automobiles | 429,705 | 6,559 | |
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| Small motorized vehicle | Mobility scooters, quad bikes | 767 | 13 | |
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| Medium vehicle | Vans, tractors | 51,257 | 741 | |
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| Large vehicle | Lorries, trucks (6+ wheels) | 37,757 | 579 | |
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| Bus | School buses, single/double-deckers | 19,244 | 200 | |
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| Emergency vehicle | Ambulances, police cars, fire trucks | 1,182 | 9 | |
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| **Overall** | | **576,374** | **8,842** | |
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--- |
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For more details , visit our [GitHub repository](https://github.com/AV-Lab/emt-dataset). |
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Our paper can be found [Here](https://huggingface.co/papers/2502.19260.) |
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For any inquires contact [[email protected]]([email protected]) or [https://huggingface.co/Murdism](https://huggingface.co/Murdism) |
