基于人体属性分析的考场行为识别

在实际监控的边缘设备中利用TSN或者3DCNN网络很难实现实时的、相对准确的监控任务。提出一种结合了人体检测和人体属性分析的考场行为识别算法。相对于以提取时空特征作视频分类算法为主流思想的行为识别,利用视频帧以人体检测和人体属性分析结合的行为识别方法更加快速准确。方法借助了多标签学习、注意力机制和特征金字塔等策略来改进任务,同时利用迁移学习对本地采集的数据集进行再训练,实验结果表明达到了主流数据集的良好性能,并在考场环境具有高效性与实用性。

Examination room behavior recognition based on human attribute analysis

In the actual monitoring edge devices, it is difficult to realize real-time and relatively accurate monitoring tasks by using TSN or 3DCNN network. An examination room behavior recognition algorithm combining human detection and human attribute analysis is proposed. Compared with the behavior recognition which takes extracting spatio-temporal features as the mainstream idea of video classification algorithm, the behavior recognition method based on video frame and the combination of human body detection and human attribute analysis is more rapid and accurate. The proposed method was improved by means of multi label learning, attention mechanism and feature pyramid. At the same time, the locally collected data sets were retrained by transfer learning. The experimental results showed that the good performance on the mainstream data sets was achieved, and it was efficient and practical in the examination room environment.