qhjqhj00 rgb-har-eval
Towards Robust Human Activity Recognition from RGB Video Stream with Limited Labeled Data — Sarker et al. (2018) (arXiv:1812.06544, 2018)
What this evaluates
Evaluates the ability of a skeleton-based BLSTM model to recognize human actions from RGB-only video streams under limited labeled data conditions, comparing against methods that use depth or inertial modalities.
Datasets
- UTD-MHAD — total 861; splits: train (-1), test (-1)
- KTH — total ?; splits: train (-1), test (-1)
Metrics
top-1 accuracy(primary) — range: percent- Percentage of correctly predicted action classes out of the total test samples.
F1 score— range: [0, 1]- Harmonic mean of precision and recall across all action classes.
Input / output format
Input: RGB video frames processed into skeleton key-points via OpenPose, fed into a 5-layer Bidirectional LSTM (BLSTM) model.
Output: Predicted action class label (classification among 27 classes for UTD-MHAD, 6 classes for KTH).
Scoring recipe
def compute_metrics(predictions, gold_labels):
accuracy = sum(p == g for p, g in zip(predictions, gold_labels)) / len(gold_labels)
f1 = f1_score(gold_labels, predictions, average='macro')
ci = bootstrap_ci(accuracy, n_bootstrap=50)
return accuracy, f1, ci
Common pitfalls
- Using depth or inertial modalities instead of strictly RGB-only as specified.
- Failing to follow the 50-50 train-test split from the original UTD-MHAD paper.
- Ignoring the requirement for full subject silhouettes, which degrades pose key-point extraction reliability.
Evidence (verbatim from paper)
We follow 50-50 train-test split similar to [[14]]. In the experiments we only use the RGB modality to evaluate our proposed method. Fig. 4 shows the comparison among all these models on accuracy and F1 score. We report confidence interval based on 50 bootstrap trials.
Citation
@misc{sarker2018robust,
title={Towards Robust Human Activity Recognition from RGB Video Stream with Limited Labeled Data},
author={Sarker et al. (2018)},
year={2018},
note={arXiv:1812.06544}
}
- arXiv: 1812.06544