Create README.md

README.md

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+---
+license: apache-2.0
+task_categories:
+- object-detection
+language:
+- en
+tags:
+- Multi-Object-Tracking
+pretty_name: HardTracksDataset
+size_categories:
+- 100K<n<1M
+---
+
+
+# HardTracksDataset: A Benchmark for Robust Object Tracking under Heavy Occlusion and Challenging Conditions
+
+[Computer Vision Lab, ETH Zurich](https://vision.ee.ethz.ch/)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/682088962c40f64d03c4bff7/FhWwBRVvkFMtfdNQ-vnVT.png)
+
+
+## Introduction 
+We introduce the HardTracksDataset (HTD), a novel multi-object tracking (MOT) benchmark specifically designed to address two critical 
+limitations prevalent in existing tracking datasets. First, most current MOT benchmarks narrowly focus on restricted scenarios, such as 
+pedestrian movements, dance sequences, or autonomous driving environments, thus lacking the object diversity and scenario complexity 
+representative of real-world conditions. Second, datasets featuring broader vocabularies, such as, OVT-B and TAO, typically do not sufficiently emphasize challenging scenarios involving long-term occlusions, abrupt appearance changes, and significant position variations. As a consequence, the majority of tracking instances evaluated are relatively easy, obscuring trackers’ limitations on truly challenging cases. HTD addresses these gaps by curating a challenging subset of scenarios from existing datasets, explicitly combining large vocabulary diversity with severe visual challenges. By emphasizing difficult tracking scenarios, particularly long-term occlusions and substantial appearance shifts, HTD provides a focused benchmark aimed at fostering the development of more robust and reliable tracking algorithms for complex real-world situations.
+
+## Results of state of the art trackers on HTD
+<table>
+  <thead>
+    <tr>
+      <th rowspan="2">Method</th>
+      <th colspan="4">Validation</th>
+      <th colspan="4">Test</th>
+    </tr>
+    <tr>
+      <th>TETA</th>
+      <th>LocA</th>
+      <th>AssocA</th>
+      <th>ClsA</th>
+      <th>TETA</th>
+      <th>LocA</th>
+      <th>AssocA</th>
+      <th>ClsA</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td colspan="9"><em>Motion-based</em></td>
+    </tr>
+    <tr>
+      <td>ByteTrack</td>
+      <td>34.877</td>
+      <td>54.624</td>
+      <td>19.085</td>
+      <td>30.922</td>
+      <td>37.875</td>
+      <td>56.135</td>
+      <td>19.464</td>
+      <td>38.025</td>
+    </tr>
+    <tr>
+      <td>DeepSORT</td>
+      <td>33.782</td>
+      <td>57.350</td>
+      <td>15.009</td>
+      <td>28.987</td>
+      <td>37.099</td>
+      <td>58.766</td>
+      <td>15.729</td>
+      <td>36.803</td>
+    </tr>
+    <tr>
+      <td>OCSORT</td>
+      <td>33.012</td>
+      <td>57.599</td>
+      <td>12.558</td>
+      <td>28.880</td>
+      <td>35.164</td>
+      <td>59.117</td>
+      <td>11.549</td>
+      <td>34.825</td>
+    </tr>
+    <tr>
+      <td colspan="9"><em>Appearance-based</em></td>
+    </tr>
+    <tr>
+      <td>MASA</td>
+      <td>42.246</td>
+      <td>60.260</td>
+      <td>34.241</td>
+      <td>32.237</td>
+      <td>43.656</td>
+      <td>60.125</td>
+      <td>31.454</td>
+      <td><strong>39.390</strong></td>
+    </tr>
+    <tr>
+      <td>OV-Track</td>
+      <td>29.179</td>
+      <td>47.393</td>
+      <td>25.758</td>
+      <td>14.385</td>
+      <td>33.586</td>
+      <td>51.310</td>
+      <td>26.507</td>
+      <td>22.941</td>
+    </tr>
+    <tr>
+      <td colspan="9"><em>Transformer-based</em></td>
+    </tr>
+    <tr>
+      <td>OVTR</td>
+      <td>26.585</td>
+      <td>44.031</td>
+      <td>23.724</td>
+      <td>14.138</td>
+      <td>29.771</td>
+      <td>46.338</td>
+      <td>24.974</td>
+      <td>21.643</td>
+    </tr>
+    <tr>
+      <td colspan="9"></td>
+    </tr>
+    <tr>
+      <td><strong>MASA+</strong></td>
+      <td><strong>42.716</strong></td>
+      <td><strong>60.364</strong></td>
+      <td><strong>35.252</strong></td>
+      <td><strong>32.532</strong></td>
+      <td><strong>44.063</strong></td>
+      <td><strong>60.319</strong></td>
+      <td><strong>32.735</strong></td>
+      <td>39.135</td>
+    </tr>
+  </tbody>
+</table>
+
+
+## Download Instructions
+
+To download the dataset you can use the HuggingFace CLI. 
+First install the HuggingFace CLI according to the official [HuggingFace documentation](https://huggingface.co/docs/huggingface_hub/main/guides/cli)
+and login with your HuggingFace account. Then, you can download the dataset using the following command:
+
+```bash
+huggingface-cli download mscheidl/htd --repo-type dataset
+```
+
+The dataset is organized in the following structure:
+
+``` 
+├── htd
+    ├── data
+        ├── AnimalTrack
+        ├── BDD
+        ├── ...
+    ├── annotations
+        ├── classes.txt
+        ├── hard_tracks_dataset_coco_test.json
+        ├── hard_tracks_dataset_coco_val.json
+        ├── ...
+    ├── metadata
+        ├── lvis_v1_clip_a+cname.npy
+        ├── lvis_v1_train_cat_info.json
+```
+
+The `data` folder contains the videos, the `annotations` folder contains the annotations in COCO (TAO) format, and the `metadata` folder contains the metadata files for running MASA+. 
+If you use HTD independently, you can ignore the `metadata` folder. 
+
+
+## Annotation format for HTD dataset
+
+
+The annotations folder is structured as follows:
+
+```
+├── annotations
+    ├── classes.txt
+    ├── hard_tracks_dataset_coco_test.json
+    ├── hard_tracks_dataset_coco_val.json
+    ├── hard_tracks_dataset_coco.json
+    ├── hard_tracks_dataset_coco_class_agnostic.json
+```
+
+Details about the annotations:
+- `classes.txt`: Contains the list of classes in the dataset. Useful for Open-Vocabulary tracking.
+- `hard_tracks_dataset_coco_test.json`: Contains the annotations for the test set.
+- `hard_tracks_dataset_coco_val.json`: Contains the annotations for the validation set.
+- `hard_tracks_dataset_coco.json`: Contains the annotations for the entire dataset.
+- `hard_tracks_dataset_coco_class_agnostic.json`: Contains the annotations for the entire dataset in a class-agnostic format. This means that there is only one category namely "object" and all the objects in the dataset are assigned to this category.
+
+
+The HTD dataset is annotated in COCO format. The annotations are stored in JSON files, which contain information about the images, annotations, categories, and other metadata.
+The format of the annotations is as follows:
+
+````python
+{
+    "images": [image],
+    "videos": [video],
+    "tracks": [track],
+    "annotations": [annotation],
+    "categories": [category]
+}
+
+image: {
+    "id": int,                            # Unique ID of the image
+    "video_id": int,                      # Reference to the parent video
+    "file_name": str,                     # Path to the image file
+    "width": int,                         # Image width in pixels
+    "height": int,                        # Image height in pixels
+    "frame_index": int,                   # Index of the frame within the video (starting from 0)
+    "frame_id": int                       # Redundant or external frame ID (optional alignment)
+    "video": str,                         # Name of the video 
+    "neg_category_ids": [int],            # List of category IDs explicitly not present (optional)
+    "not_exhaustive_category_ids": [int]  # Categories not exhaustively labeled in this image (optional)
+        
+video: {
+    "id": int,                            # Unique video ID
+    "name": str,                          # Human-readable or path-based name
+    "width": int,                         # Frame width
+    "height": int,                        # Frame height
+    "neg_category_ids": [int],            # List of category IDs explicitly not present (optional)
+    "not_exhaustive_category_ids": [int]  # Categories not exhaustively labeled in this video (optional)
+    "frame_range": int,                   # Number of frames between annotated frames
+    "metadata": dict,                     # Metadata for the video    
+}
+        
+track: {
+    "id": int,             # Unique track ID
+    "category_id": int,    # Object category
+    "video_id": int        # Associated video
+}
+        
+category: {
+    "id": int,            # Unique category ID
+    "name": str,          # Human-readable name of the category
+}
+        
+annotation: {
+    "id": int,                    # Unique annotation ID
+    "image_id": int,              # Image/frame ID
+    "video_id": int,              # Video ID
+    "track_id": int,              # Associated track ID
+    "bbox": [x, y, w, h],         # Bounding box in absolute pixel coordinates
+    "area": float,                # Area of the bounding box
+    "category_id": int            # Category of the object
+    "iscrowd": int,               # Crowd flag (from COCO)
+    "segmentation": [],           # Polygon-based segmentation (if available)
+    "instance_id": int,           # Instance index with a video
+    "scale_category": str         # Scale type (e.g., 'moving-object')
+}
+````