Optimized Skeleton graph based CNN for Human Abnormal Detection in Video Streams

Abstract

Human Action Recognition (HAR) is the process of understanding human actions and behavior. HAR has a broad range of applications, and it has been focused on increasing the attention in various domain of computed vision. Abnormal detection from video stream is vigorous to guarantee the security in both outside spaces with the internal. Furthermore, the abnormal actions are really infrequent and rare, which makes the supervision process more challenging and difficult. In this research, skeleton graph-based Convolutional Neural Network (CNN) is devised for human abnormal activity detection. Here, the skeleton graph-based CNN (Skeleton graph_CNN) is devised based on the concept of classical convolution and skeleton graph generation. The human action recognition classifies the human actions into normal and abnormal class. The abnormal actions from the recognized outcome are detected with Skeleton graph_CNN, which provides the various actions of human as an output. The Skeleton graph_CNNgenerates the skeleton shaped human structure by connecting the joints within the frame to consecutive frames. Moreover, the HAR is carried out using IITB-Corridor Dataset based on metrics, such as testing accuracy of 0.961, sensitivity of 0.956 and specificity of 0.960, correspondingly.