People Detection and Tracking Using the Explorative Particle Filtering

Abstract

Automatic people detection and tracking is a very essential task of video surveillance systems. It can improve a system's performance in important fields such as security, safety, human activity monitoring etc. In this paper we present a novel approach for people detection and 3D tracking. Our method is based on a human upper body 3D model and a likelihood function to evaluate its presence in a certain region of the scene. We then find the maxima of this function using a modified particle filtering algorithm which we call Explorative Particle Filtering (ExPF). We designed this algorithm in a way to guarantee a multiple objects tracking and a good estimation of their positions when using a small number of particles. Our technique is generic and simple as no dynamic models nor trained features models (color, shape etc.) were used. We also show some tracking results from video surveillance feeds in order to illustrate our approach.