# Frame Auc Eval

> Evaluates a model's ability to detect abnormal human activities by predicting multi-timescale future and past pose trajectories. The framework measures prediction errors across different temporal granularities and combines them to identify anomalous frames. Use when the user wants to benchmark on HR-ShanghaiTech, HR-Avenue, Corridor, or asks about evaluating this task. Reports Frame-AUC.

- Skill: `qhjqhj00/frame-auc-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/frame-auc-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/frame-auc-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/frame-auc-eval

---


# frame-auc-eval

> Multi-timescale Trajectory Prediction for Abnormal Human Activity Detection — Rodrigues et al. (2019) (arXiv:1908.04321, 2019)

## What this evaluates

Evaluates a model's ability to detect abnormal human activities by predicting multi-timescale future and past pose trajectories. The framework measures prediction errors across different temporal granularities and combines them to identify anomalous frames.

## Datasets

- **HR-ShanghaiTech** — total ?; splits: train (274515), test (42883)
- **HR-Avenue** — total ?; splits: train (15328), test (15324)
- **Corridor** — total ?; splits: train (301999), test (181567)

## Metrics

- `Frame-AUC` **(primary)** — range: percent
  - Area under the Receiver Operating Characteristic (ROC) curve computed at the frame level. Binary predictions are generated by comparing combined multi-timescale pose prediction errors against a fixed threshold.

## Input / output format

**Input**: Sequences of human pose trajectories (25 joints with confidence scores) extracted from videos, split into variable lengths (6, 10, 26, 50) corresponding to different prediction timescales.

**Output**: Binary anomaly label per frame (normal/abnormal) derived from voting across multi-timescale prediction errors compared to a threshold.

## Scoring recipe

```python
errors = []
for timescale in [3, 5, 13, 25]:
    pred = model.predict(pose_seq, timescale)
    err = weighted_mse(pred, gt, confidence)
    errors.append(err)
combined_error = vote(errors)
pred_label = 1 if combined_error > threshold else 0
frame_auc = compute_auc(gt_labels, pred_labels)
```

## Common pitfalls

- The evaluation is strictly human-centric; non-human anomalies (e.g., vehicles) are ignored.
- Timescale 25 may degrade performance on datasets lacking long-term anomalies (e.g., HR-Avenue), as noted in ablation studies.
- Pose trajectories rely on external detectors and trackers, so evaluation performance is bounded by pre-processing accuracy.

## Evidence (verbatim from paper)

> To compare with these existing approaches, we also use Frame-AUC as the evaluating criteria. ... Finally at any time instant, if the error value is higher than a threshold, it is considered as abnormal.

## Citation

```bibtex
@misc{rodrigues2019multi,
  title={Multi-timescale Trajectory Prediction for Abnormal Human Activity Detection},
  author={Rodrigues et al. (2019)},
  year={2019},
  note={arXiv:1908.04321}
}
```

- arXiv: 1908.04321