Update README.md

README.md

@@ -14,18 +14,17 @@ size_categories:
 
 [Computer Vision Lab, ETH Zurich](https://vision.ee.ethz.ch/)
 
-
-<p align="center">
-    <img src="./docs/imgs/main.png" alt="Image" width="100%"/>
-</p>
+![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.
+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>
-  <caption>TETA evaluation of state-of-the-art trackers on the HTD validation and test sets, grouped by tracking approach.</caption>
   <thead>
     <tr>
       <th rowspan="2">Method</th>
@@ -139,10 +138,6 @@ We introduce the HardTracksDataset (HTD), a novel multi-object tracking (MOT) be
 
 ## Download Instructions
 
-We provide the full dataset with annotations and metadata on HuggingFace:
-
-- [HTD Dataset 🤗](https://huggingface.co/datasets/mscheidl/htd)
-
 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:
@@ -170,4 +165,90 @@ The dataset is organized in the following structure:
 ```
 
 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. More information about the annotation format please refer to [ANNOTATIONS.md](docs/ANNOTATIONS.md).
+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')
+}
+````
+
+