Semi-supervised learning suffers from the imbalance of labeled and unlabeled
training data in the video surveillance scenario. In this paper, we propose a
new semi-supervised learning method called SIAVC for industrial accident video
classification. Specifically, we design a video augmentation module called the
Super Augmentation Block (SAB). SAB adds Gaussian noise and randomly masks
video frames according to historical loss on the unlabeled data for model
optimization. Then, we propose a Video Cross-set Augmentation Module (VCAM) to
generate diverse pseudo-label samples from the high-confidence unlabeled
samples, which alleviates the mismatch of sampling experience and provides
high-quality training data. Additionally, we construct a new industrial
accident surveillance video dataset with frame-level annotation, namely ECA9,
to evaluate our proposed method. Compared with the state-of-the-art
semi-supervised learning based methods, SIAVC demonstrates outstanding video
classification performance, achieving 88.76\% and 89.13\% accuracy on ECA9 and
Fire Detection datasets, respectively. The source code and the constructed
dataset ECA9 will be released in https://github.com/AlchemyEmperor/SIAVC.

提出了一种新的半监督学习方法 SIAVC，用于工业事故视频分类，通过视频增强和伪标签样本生成来改善半监督学习中标记和未标记训练数据的不平衡问题，并在构建的 ECA9 数据集上取得优异的视频分类性能。