Optical Flow Estimation Eval

---
name: optical-flow-estimation-eval
description: Evaluates the accuracy of predicted optical flow fields against ground truth motion vectors between consecutive image frames. It probes a model's ability to estimate dense pixel-wise displacement in both synthetic cinematic scenes and real-world driving environments. Use when the user wants to benchmark on FlyingChairs, Sintel, KITTI12, KITTI15, Middlebury, or asks about evaluating this task. Reports AEE.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 1805.07036
  bibtex_key: [REDACTED]
  confidence: high
---

# optical-flow-estimation-eval

> LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation — Hui et al. (2018) (arXiv:1805.07036, 2018)

## What this evaluates

Evaluates the accuracy of predicted optical flow fields against ground truth motion vectors between consecutive image frames. It probes a model's ability to estimate dense pixel-wise displacement in both synthetic cinematic scenes and real-world driving environments.

## Datasets

- **FlyingChairs** — total ?; splits: test (-1)
- **Sintel** — total ?; splits: clean (-1), final (-1)
- **KITTI12** — total ?; splits: test (-1)
- **KITTI15** — total ?; splits: test (-1)
- **Middlebury** — total ?; splits: test (-1)

## Metrics

- `AEE` **(primary)** — range: other
  - Average End-Point Error. Computed as the mean Euclidean distance between the predicted and ground truth flow vectors across all pixels: AEE = (1/N) * sum(||f_pred - f_gt||_2).
- `Fl-all` — range: percent
  - Percentage of outliers averaged over all pixels. A pixel is considered an inlier if its EPE is less than 3 pixels or less than 5% of the ground truth flow magnitude; Fl-all is the complement percentage.

## Input / output format

**Input**: Pairs of consecutive frames (RGB images) from a video sequence.

**Output**: A dense optical flow field (H x W x 2 tensor) representing the horizontal and vertical displacement vectors for each pixel.

## Scoring recipe

```python
def compute_aee(pred_flow, gt_flow):
    epe = np.sqrt(np.sum((pred_flow - gt_flow)**2, axis=-1))
    return np.mean(epe)

def compute_fl_all(pred_flow, gt_flow):
    epe = np.sqrt(np.sum((pred_flow - gt_flow)**2, axis=-1))
    gt_mag = np.linalg.norm(gt_flow, axis=-1)
    inlier = (epe < 3.0) | (epe < 0.05 * gt_mag)
    return 100.0 * (1.0 - np.mean(inlier))
```

## Common pitfalls

- The paper explicitly notes that results in parentheses are computed on training data and are 'not directly comparable to the others' (test set results).
- Runtime measurements vary significantly by framework (Torch vs Caffe); comparing speeds across models requires noting the implementation used.
- The outlier threshold for Fl-all uses a logical OR between 3px and 5% of flow magnitude; using only one threshold will yield different values.

## Evidence (verbatim from paper)

> Average end-point error (AEE) is reported. ... Fl-all: Percentage of outliers averaged over all pixels. Inliers are defined as EPE < 3 pixels or < 5%.

## Citation

```bibtex
@misc{hui2018liteflownet,
  title={LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation},
  author={Hui et al. (2018)},
  year={2018},
  note={arXiv:1805.07036}
}
```

- arXiv: 1805.07036