Datasets:

KuAvLab
/

EMT

@@ -1,88 +1,117 @@
 ---
 license: cc-by-nc-sa-4.0
 ---
 ## Introduction
-EMT is a comprehensive dataset for autonomous driving research, containing 57 minutes of diverse urban traffic footage from the Gulf Region. The dataset provides rich semantic annotations across two agent categories: people (pedestrians and cyclists), vehicles (seven classes). Each video segment spans 2.5-3 minutes, capturing challenging real-world scenarios:
-- **Dense Urban Traffic**: Complex multi-agent interactions in congested scenarios
-- **Weather Variations**: Clear and rainy conditions
-- **Visual Challenges**: High reflections from road surfaces and adverse weather combinations (rainy nights)
-The dataset provides dense annotations for:
-- **Detection & Tracking**: Multi-object tracking with consistent IDs - Available here
-- **Trajectory Prediction**: Future motion paths and social interactions - Refer to the github repo
-- **Intention Prediction**: Behavior understanding in complex scenarios - Refer to the github repo
-Validated through benchmarking on state-of-the-art models across tracking, trajectory prediction, and intention prediction tasks, with corresponding ground truth annotations for each benchmark.
-## Quick start
-``` bash
 from datasets import load_dataset
-# Load the dataset again with the 'force_redownload' option
-dataset = load_dataset("KuAvLab/EMT", split="train")#download_mode="reuse_cache_if_exists")# download_mode="force_redownload") or split train
-```
-### Avaiable labels:
-- Data from datatset has two outputs: image and object:
-- Image contains the frame image while object contains annotation:
-``` # object labels
-bbox: bbox of detected objects
-track_id: tracking id of detected object
-class_id: class id of object
-class_name: type of object
 ```
-Sample use case:
 ```python
 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:", len(data['objects']['bbox']),"\nTrack IDs:", data['objects']['track_id']),"\nclass IDs:", data['objects']['class_id']) )
 ```
-### Data Collection
-| Aspect | Description |
-|:-------|:------------|
-| Duration | 57 minutes total footage |
-| Segments | 2.5-3 minutes continuous recordings |
-| FPS | 10fps for annotated frames |
-| Agent Classes | 2 Person classes and 7 Vehicle classes|
 ### Agent Categories
-1. **People**
-   - Pedestrians
-   - Cyclists
-2. **Vehicles**
-   - Motorbike
-   - Small motorised 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** | **Description** | **Number of Bounding Boxes** | **Number of Agents** |
-|-----------|----------------|------------------------------|----------------------|
-| Pedestrian | An individual walking on foot. | 24,574 | 568 |
-| Cyclist | Any bicycle or electric bike rider. | 594 | 14 |
-| Motorbike | Includes motorcycles, bikes, and scooters with two or three wheels. | 11,294 | 159 |
-| Car | Any standard automobile. | 429,705 | 6,559 |
-| Small motorized vehicle | Motorized transport smaller than a car, such as mobility scooters and quad bikes. | 767 | 13 |
-| Medium vehicle | Includes vehicles larger than a standard car, such as vans or tractors. | 51,257 | 741 |
-| Large vehicle | Refers to vehicles larger than vans, such as lorries, typically with six or more wheels. | 37,757 | 579 |
-| Bus | Covers all types of buses, including school buses, single-deck, double-deck. | 19,244 | 200 |
-| Emergency vehicle | Emergency response units like ambulances, police cars, and fire trucks, distinguished by red and blue flashing lights. | 1,182 | 9 |
-| **_Overall:_** | | **576,374** | **8,842** |

 ---
 license: cc-by-nc-sa-4.0
 ---
+---
+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** – 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:", len(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).