Crowd behavior analysis using MoDTA approach

Abstract

In order to analyze the behaviors of human, significant extent of work has been carried out in the video surveillance applications. While considering the crowded scenes, the adopted features are crafted manually which have a great side to detect anomaly. It requires prior information and is hard to extract from complex video scenes and also it involves huge computational costs. In this paper, we are proposing multi-observational detection and tracking approach (MoDTA) that is based on observational filter. The MoDTA initially acquires  people location in an image, so that is can detect conviction value at pointed locations which generally increases with respect to people density. In the phase of tracking, MoDTA computes the multiple observed weight values and individual features, also advection particle is used at motion model in order to facilitate the dense scenario tracking. Coefficient of correlation is used as template detector and the function of template detector is to estimate the upcoming object. Our proposed MoDTA is compared with other existing detection and tracking methods in order to evaluate the system performance.