Abstract
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 \url{this https URL}.
Abstract (translated)
半监督学习在视频监控场景中受到有标签和无标签训练数据之间不平衡的影响。在本文中，我们提出了一种名为SIAVC的新半监督学习方法，用于工业事故视频分类。具体来说，我们设计了一个视频增强模块，称为Super Augmentation Block（SAB）。SAB在无标签数据上根据历史损失添加高斯噪声并随机遮盖视频帧。然后，我们提出了一个视频跨样本增强模块（VCAM），从高置信度的无标签样本中生成多样性的伪标签样本，减轻了抽样体验的差异，并为高质量训练数据提供了支持。此外，我们还构建了一个新的工业事故监控视频数据集，以评估我们所提出的方法。与最先进的半监督学习方法相比，SIAVC表现出出色的视频分类性能，在ECA9和火灾检测数据集上分别获得了88.76%和89.13%的准确率。源代码和构建的数据集ECA9将发布在https://这个URL上。
URL
https://arxiv.org/abs/2405.14506
