Update README.md

README.md

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-license: cc-by-nc-4.0
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+---
+license: cc-by-nc-4.0
+---
+# SportsGrounding Dataset
+
+## 1. Task Description
+The Spatio-Temporal Video Grounding (STVG) task aims to take an untrimmed video and a natural language description as input, and output the spatio-temporal tube corresponding to the natural language description—specifically, locating the start and end frames, as well as the bounding boxes of the target within the located segment.
+
+SportsGrounding is based on the basketball subset of the MultiSports dataset, focusing on basketball-related scenarios. It contains numerous complex interaction scenes between people and between people and objects, with a larger number of instances.
+## 2. Data Overview
+The dataset is modified based on the video and annotation data of MultiSports. Six videos with excessive repetitive actions that cannot be distinguished by natural language are removed, resulting in a total of 520 videos. It follows the train/val split of MultiSports, with 374 videos in the training set and 146 videos in the validation set. Unlike other STVG datasets, each video in SportsGrounding contains multiple captions describing different target persons.
+
+### Key Statistics of SportsGrounding
+| Metric | Value |
+|--------|-------|
+| Dataset Size | 4243 tubes (i.e., 4243 video-text pairs) |
+| Average Video Duration | 19.70 seconds |
+| Average Tube Duration | 1.49 seconds |
+| Average Description Length | 16.89 words |
+
+### Comparison with Other Datasets
+| Metric | VidSTG | HC-STVG v1 | HC-STVG v2 | SportsGrounding |
+|--------|--------|------------|------------|-----------------|
+| Data Source | VidOR | AVA | - | MultiSports |
+| Dataset Size | 99943 video-text pairs | 5660 video-text pairs | 16544 video-text pairs | 4243 video-text pairs |
+| Average Video Duration | 28.01 seconds | 20 seconds | - | 19.70 seconds |
+| Average Tube Duration | 9.68 seconds | 5.37 seconds | - | 1.49 seconds |
+| Average Description Length | Declarative: 11.12; Interrogative: 8.98 | 17.25 | - | 16.89 |
+
+### Unique Characteristics of SportsGrounding
+- Some instances have very short durations.
+- More people appear in the videos (other datasets contain fewer people even in multi-person scenarios; 57.2% of videos in HC-STVG have more than 3 people, and the rest have 2 people).
+- More complex interactions between people; many descriptions require certain reasoning and scene information modeling.
+  Example: "The defender in white is blocked by the teammate of this offensive player."
+
+## 3. Data Format
+Annotation files are divided into `train.json` and `val.json`. Each annotation entry is structured as follows:
+
+```python
+{
+  "v_00HRwkvvjtQ_c001.mp4": [
+    {
+      "bbox": [[x0, y0, w0, h0], [x1, y1, w1, h1], ...],  # Continuous bounding boxes of the target person
+      "fps": 25,  # Frames per second of the video
+      "st_frame": 7,  # Ground truth start frame (index starts at 1)
+      "ed_frame": 28,  # Ground truth end frame
+      "caption": "The player in white attempts to gain control of the ball after an official tosses it into the air between him and his opponent in blue.",  # Caption describing the target person
+      "width": 1080,  # Image width
+      "height": 720  # Image height
+    },
+    ...
+  ],
+  ...
+}
+```