Classification of human activity detection based on an intelligent regression model in video sequences

Abstract

The most critical objective in security surveillance is abnormal event detection in public scenarios. A scheme is presented for detecting abnormal behaviours in the activities of human groups based on social behaviour analysis. This approach efficiently models group activities than some of the previous strategies that use independent local features. This paper presents a feature descriptor method to signify the movement by implementing the optical flow through covariance matrix coding. The multi-RoI (region of interest) covariance matrix has some frames or patches which could represent the movement in high accuracy. Normal samples are plentiful in public surveillance videos, while there are only a few abnormal samples. For that, the model of a hybridised optical flow covariance matrix is represented in this paper. Optical flow (OF) in the temporal domain is measured as a critical feature of video streams. The logistic regression method is used to detect abnormal activities in a crowded scene. Finally, the behaviours of human crowds can be predicted using benchmark datasets such as UMN, UCSD as well as BEHAVE. The obtained experimental results show that the proposed approach can effectively detect abnormal events from the abandoned environment of surveillance videos.