qhjqhj00 hardvs2.0-eval
Human Activity Recognition using RGB-Event based Sensors: A Multi-modal Heat Conduction Model and A Benchmark Dataset — Shiao Wang et al. (arXiv:2504.05830, 2025)
What this evaluates
Evaluates multi-modal human activity recognition capabilities by classifying 300 action categories from synchronized RGB frames and asynchronous event streams under challenging real-world conditions such as low light, occlusion, and dynamic backgrounds.
Datasets
- HARDVS 2.0 — total 107646; splits: train (64526), val (10734), test (32386); repo https://github.com/Event-AHU/HARDVS
Metrics
accuracy(primary) — range: [0, 1]- Standard top-1 classification accuracy: the proportion of correctly predicted class labels out of the total number of test instances.
Input / output format
Input: Paired RGB video frames and asynchronous event streams captured at 346×260 resolution, with each sequence lasting approximately 5 seconds.
Output: A single discrete class label corresponding to one of the 300 predefined human activity categories.
Scoring recipe
correct = 0
total = len(predictions)
for pred, gold in zip(predictions, gold_labels):
if pred == gold:
correct += 1
return correct / total
Common pitfalls
- The dataset enforces a strict 60/10/30 split per category rather than a global random split, so models must be evaluated on category-balanced test sets.
- Inputs consist of dual-modality data (RGB frames + event streams), requiring specialized fusion architectures; standard RGB-only or event-only baselines are not directly comparable.
Evidence (verbatim from paper)
We split 60%,10%, and 30% of each category for training, validating, and testing, respectively. In total, the number of videos in the training, validating, and testing subsets are 64526, 10734, and 32386, respectively.
Citation
@misc{wang2025hardvs,
title={Human Activity Recognition using RGB-Event based Sensors: A Multi-modal Heat Conduction Model and A Benchmark Dataset},
author={Shiao Wang et al.},
year={2025},
note={arXiv:2504.05830}
}
- arXiv: 2504.05830