Experimental Analysis of Human Behavior Monitoring System using Gaze Shift Principles and Bayesian Image Foraging Logic

To gather the most relevant data as well as information for a given activity, this paper looks at the challenge of focusing our attention on subsets of video streams. This is a foraging problem, which is how the challenge of monitoring is classified. Observer attentiveness may be modeled as foraging behavior using the developed probabilistic framework. The forager changes its focus from one stream or camera to the next based on what it sees, whether it's fascinating items or activity. Multi-stream video summary can benefit from the technique shown here. Furthermore, it may be used in conjunction with advanced video surveillance, such as activity and behavior analysis, as a first stage in the process. As a demonstration of the suggested technique's utility, findings from an experiment using a publicly available data set are offered.