A human behavior analysis model to track object behavior in surveillance videos

Abstract

In potential disasters, real world scenarios and public events, understanding of crowd psychology is a challenging process and detection of crowd behaviors in those events is quite complicated. Therefore, in this article, an effective crowd analysis mechanism is introduced using human behavior analysis model based on motion heat flow enabled optical flow method. Here, OCP descriptor evaluates required object points from motion heat map to detect objects and generate feature weights. Here, three type of histogram gradients are evaluated using proposed HBA model such as inconsistencies between neighboring points and OCP descriptor, gradients obtained considering spatial angle and gradients obtained considering temporal angle. Features obtained considering spatial as well as temporal domain are encoded using feature encoding scheme. The performance of proposed Human behavior Analysis (HBA) model is tested on Web and Violent-Flow Video Dataset. The performance of the proposed HBA model is compared against several traditional behavior analysis methods considering performance matrices like Accuracy, Precision and Recall to detect behaviors in public events. Performance of proposed HBA model is superior to multiple state-of-art-crowd behavior analysis techniques.

Keywords

• Human behavior analysis (HBA)
• Violent-flow dataset
• Web dataset
• Feature encoding and optical flow based motion heat map