Skeleton Based Abnormal Behavior Recognition Using Spatio-Temporal Convolution and Attention-Based LSTM
Kai Zhou, Tingting Wu, Chunyu Wang, Junfeng Wang, Chao Li
Abstract
Behavior recognition is a well-known computer version technology, it has been used in many applications such as video surveillance, robotics, human-computer interaction and sports video, etc. However, most of existing works ignored the depth information so that they resulted in over-fitting and the inferior performance. Consequently, a novel framework for behavior recognition is proposed which is based on spatio-temporal convolution and attention-based LSTM (ST-CNN & ATT-LSTM). In this framework, deep spatial information is merged into each segment, meanwhile we focus on the method of key information extraction, which is essential for improving behavior recognition performance. Finally, the proposed framework is evaluated with real world surveillance video data, and the results indicate that our framework is superior to existing methods.