# Kinect Action Recognition Eval

> Evaluates the robustness of Kinect-based action recognition algorithms across single-view and cross-view scenarios. Probes how well models handle viewpoint variation, motion variability, and different sensor modalities (depth, skeleton, RGB-D) on standardized benchmarks. Use when the user wants to benchmark on MSRAction3D Dataset, 3D Action Pairs Dataset, Cornell Activity Dataset (CAD-60), UWA3D Single View Dataset, UWA3D Multiview Dataset, or asks about evaluating this task. Reports average recognition accuracy.

- Skill: `qhjqhj00/kinect-action-recognition-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/kinect-action-recognition-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/kinect-action-recognition-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/kinect-action-recognition-eval

---


# kinect-action-recognition-eval

> Analysis and Evaluation of Kinect-based Action Recognition Algorithms — Lei Wang (2021) (arXiv:2112.08626, 2021)

## What this evaluates

Evaluates the robustness of Kinect-based action recognition algorithms across single-view and cross-view scenarios. Probes how well models handle viewpoint variation, motion variability, and different sensor modalities (depth, skeleton, RGB-D) on standardized benchmarks.

## Datasets

- **MSRAction3D Dataset** — total ?; splits: train (-1), test (-1)
- **3D Action Pairs Dataset** — total ?; splits: train (-1), test (-1)
- **Cornell Activity Dataset (CAD-60)** — total ?; splits: train (-1), test (-1)
- **UWA3D Single View Dataset** — total ?; splits: train (-1), test (-1)
- **UWA3D Multiview Dataset** — total ?; splits: train (-1), test (-1)

## Metrics

- `average recognition accuracy` **(primary)** — range: [0, 1]
  - Calculated per class as the number of correctly predicted labels for that class divided by the total number of ground-truth labels for that class in the dataset. Overall performance is typically summarized via a confusion matrix or averaged across classes.

## Input / output format

**Input**: Depth video sequences, 3D skeleton joint coordinates, and/or RGB-D frames captured by Kinect v1.

**Output**: Predicted action class label for each input sequence or frame.

## Scoring recipe

```python
def compute_accuracy(predictions, gold_labels):
    correct = sum(1 for p, g in zip(predictions, gold_labels) if p == g)
    total = len(gold_labels)
    return correct / total if total > 0 else 0.0
```

## Common pitfalls

- Spatiotemporal cell sizes and desired frame counts for skeleton representation vary significantly across datasets and must be manually adjusted per dataset to match original implementations.
- The HDG algorithm requires combining four specific feature vectors (hod, hodg, jpd, jmv) rather than using them individually, and its original code was unavailable, requiring custom implementation.
- Cross-view recognition strategies are only applicable to the UWA3D Multiview Dataset, while the other four datasets are strictly single-view.

## Evidence (verbatim from paper)

> Average recognition accuracy for a given class A is given by average recognition accuracy = (# correct class A labels from algorithm) / (# total class A labels in dataset). The measure given in Equation 4.1 is for one class only. To illustrate the recognition accuracy of an algorithm, a confusion matrix shown the recognition accuracy of all classes is often used.

## Citation

```bibtex
@misc{wang2021kinectactionrecognition,
  title={Analysis and Evaluation of Kinect-based Action Recognition Algorithms},
  author={Lei Wang (2021)},
  year={2021},
  note={arXiv:2112.08626}
}
```

- arXiv: 2112.08626