# Pairwise Interaction Eval

> Evaluates a robot's ability to detect interacting human pairs and classify their coarse-grained interaction types (e.g., walking, standing, sitting together) using bounding box geometry and optical flow, without relying on costly skeleton-based pose estimation. Use when the user wants to benchmark on JRDB, Collective Activity Dataset (CAD), Lawnmower Dataset, or asks about evaluating this task. Reports Accuracy.

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

---


# pairwise-interaction-eval

> Detection and Recognition: A Pairwise Interaction Framework for Mobile Service Robots — Liang et al. (2026) (arXiv:2602.22346, 2026)

## What this evaluates

Evaluates a robot's ability to detect interacting human pairs and classify their coarse-grained interaction types (e.g., walking, standing, sitting together) using bounding box geometry and optical flow, without relying on costly skeleton-based pose estimation.

## Datasets

- **JRDB** — total ?; splits: train (-1), test (-1)
- **Collective Activity Dataset (CAD)** — total ?; splits: train (-1), test (-1)
- **Lawnmower Dataset** — total ?; splits: test (-1)

## Metrics

- `Accuracy` **(primary)** — range: [0, 1] or percent
  - Overall fraction of correctly classified pairwise interactions: sum of correct predictions divided by total number of predictions.
- `Mean Per-Class Accuracy (MPCA)` — range: [0, 1] or percent
  - Average of per-class recall across C classes: (1/C) * sum(TP_c / N_c). Accounts for class imbalance.
- `Macro F1-score` — range: [0, 1]
  - Average of per-class F1 scores: (1/C) * sum(2 * P_c * R_c / (P_c + R_c)), where P_c and R_c are per-class precision and recall.

## Input / output format

**Input**: Video frames with bounding box annotations for individuals, plus optical flow and geometric cues (spatial proximity).

**Output**: Predicted pairwise interaction type (e.g., walking, standing, sitting together) and group membership/activity labels.

## Scoring recipe

```python
def compute_metrics(preds, golds, C):
    N = len(golds)
    accuracy = sum(1 for p, g in zip(preds, golds) if p == g) / N
    
    tp = [0] * C
    n = [0] * C
    for p, g in zip(preds, golds):
        n[g] += 1
        if p == g:
            tp[g] += 1
            
    mpca = sum(tp[c] / n[c] for c in range(C) if n[c] > 0) / C
    
    macro_f1 = 0
    for c in range(C):
        p_c = tp[c] / (tp[c] + (n[c] - tp[c])) if (tp[c] + (n[c] - tp[c])) > 0 else 0
        r_c = tp[c] / n[c] if n[c] > 0 else 0
        f1_c = 2 * p_c * r_c / (p_c + r_c) if (p_c + r_c) > 0 else 0
        macro_f1 += f1_c
    macro_f1 /= C
    return accuracy, mpca, macro_f1
```

## Common pitfalls

- Class imbalance is significant, requiring weighted sampling and focal loss during training, which affects metric interpretation.
- Group-level activity is derived via majority voting over pairwise predictions, which can mask individual interaction errors and inflate group accuracy.
- Zero-shot evaluation on the lawnmower dataset suffers from domain shift (platform motion, low resolution, grass occlusion), making direct comparison with static-camera benchmarks misleading.

## Evidence (verbatim from paper)

> We report Accuracy, Mean Per-Class Accuracy (MPCA), and Macro F1-score to account for class imbalance:

|  | $\text{Accuracy}\=\frac{\sum_{i\=1}^{N}[\hat{y}_{i}\=y_{i}]}{N}$ |  | (12) |
| --- | --- | --- | --- |

|  | $\text{MPCA}\=\frac{1}{C}\sum_{c\=1}^{C}\frac{TP_{c}}{N_{c}}\=\frac{1}{C}\sum_{c\=1}^{C}\text{Recall}_{c}$ |  | (13) |
| --- | --- | --- | --- |

|  | $\text{Macro-F1}\=\frac{1}{C}\sum_{c\=1}^{C}\frac{2\cdot P_{c}\cdot R_{c}}{P_{c}+R_{c}}$ |  | (14) |

where $P_{c}\=TP_{c}/(TP_{c}+FP_{c})$ and $R_{c}\=TP_{c}/(TP_{c}+FN_{c})$ are per-class precision and recall, respectively.

## Citation

```bibtex
@misc{liang2026detection,
  title={Detection and Recognition: A Pairwise Interaction Framework for Mobile Service Robots},
  author={Liang et al. (2026)},
  year={2026},
  note={arXiv:2602.22346}
}
```

- arXiv: 2602.22346