Sportmot Eval

---
name: sportmot-eval
description: Evaluates multi-object tracking performance in sports scenes, specifically probing a model's ability to maintain track identities under fast, variable-speed motion and highly similar player appearances. Use when the user wants to benchmark on SportsMOT, or asks about evaluating this task. Reports HOTA.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2304.05170
  bibtex_key: cui2023sportmot
  confidence: high
---

# sportmot-eval

> SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes — Cui et al. (2023) (arXiv:2304.05170, 2023)

## What this evaluates

Evaluates multi-object tracking performance in sports scenes, specifically probing a model's ability to maintain track identities under fast, variable-speed motion and highly similar player appearances.

## Datasets

- **SportsMOT** — total ?; splits: train (-1), test (-1)

## Metrics

- `HOTA` **(primary)** — range: [0, 100]
  - Higher Order Tracking Accuracy combines detection accuracy (DetA) and association accuracy (AssA) by averaging their F1-scores across multiple IoU thresholds.
- `MOTA` — range: percent
  - Multi-Object Tracking Accuracy measures tracking performance by penalizing false positives, false negatives, and identity switches relative to the total number of ground truth frames.

## Input / output format

**Input**: Video sequences of sports scenes (basketball, volleyball, football) with ground truth bounding boxes and track IDs per frame.

**Output**: Per-frame bounding boxes with assigned track IDs for each detected object.

## Scoring recipe

```python
def compute_mot_metrics(pred_tracks, gt_tracks):
    # 1. Compute IoU between predicted and ground truth boxes per frame
    # 2. Match tracks using greedy/Hungarian matching across frames
    # 3. Calculate DetA and AssA F1-scores across IoU thresholds [0.5, 0.95]
    # 4. HOTA = geometric mean of DetA and AssA averaged over thresholds
    # 5. MOTA = 1 - (FN + FP + IDS) / GT_frames
    return hota_score, mota_score
```

## Common pitfalls

- Assuming linear motion models (e.g., standard Kalman filters) work well; sports motion is highly non-linear and variable-speed.
- Focusing only on detection accuracy while neglecting appearance-based association, which is critical due to similar player uniforms.
- Evaluating on private detection protocols without standardizing the detector, leading to unfair comparisons.

## Evidence (verbatim from paper)

> The wide range of HOTA and MOTA denotes SportsMOT is more distinguishable among different kinds of algorithms.

## Citation

```bibtex
@misc{cui2023sportmot,
  title={SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes},
  author={Cui et al. (2023)},
  year={2023},
  note={arXiv:2304.05170}
}
```

- arXiv: 2304.05170