Tracking Multi-Objects in Web Camera Video Using Particle Filtering

Abstract

This paper presents new methods for efficient object tracking in web camera video sequences using multiple features and particle filtering. Particle filtering is particularly useful in dealing with nonlinear state space models and non-Gaussian probability density functions. We develop a multi-objects tracing system which considers color information, distance transform based shape information and also nonlinearity. We examine the difficulties of video based tracking and step by step we analyze these issues. In our first approach, we develop the color based particle filter tracker that relies on the deterministic search of window, whose color content matches a reference histogram model. A simple HSV histogram-based color model is used to develop this observation system. Secondly, we describe a new approach for moving multi-object tracking with particle filter by shape information. The shape similarity between a template and estimated regions in the video scene is measured by their normalized cross-correlation of distance transformed images. Our observation system of particle filter is based on shape from distance transformed edge features. Template is created instantly by selecting any object from the video scene by a rectangle. Finally, in this paper we illustrate how our system is improved by using both these two cues with nonlinearity.