关键词: 人体关节点, 周期性, 多人吸烟动作识别

Abstract:

Smoking detection has become an important measure for smoking bans in public places. Video image-based smoking action recognition has been widely used in smoking detection. Deep learning method for action recognition requires a large number of data sets to train. The accuracy and real-time of the existing smoking action recognition are weak, and smoking action recognition is for a single person. To address these problems, this paper proposes a method for recognizing smoking actions by detecting periodic actions. Through a large number of experiments, the smoking behavior is rhythmic and repetitive is found. This paper analyzes periodicity of smoking actions and formulates smoking action norms. This work uses information of human joint points to track action trajectory of the human body. Smoking action recognition is realized by detecting periodicity action. Recognition of smoking actions of multiple people is realized by tracking information of multi-person. Experimental results show that the method achieve an accuracy rate of 91%, and maintains high accuracy and robustness in various scenarios.

Key words: human joint point, periodicity, multi-person smoking action recognition