A particular object tracking in an environment of multiple moving objects

Abstract

Usually, the video based object tracking deal with non-stationary image stream that changes over time. Robust and Real time moving object tracking is a problematic issue in computer vision research area. Multiple object tracking has many practical applications in scene analysis for automated surveillance. If we can track a particularly selected object in an environment of multiple moving objects, then there will be a variety of applications. In this paper, we introduce a particular object tracking in an environment of multiple moving objects. When tracking, we need to analyze video sequences to track object in each frame. In this paper, we use a differential image of region-based tracking method for the detection of multiple moving objects. In other to ensure accurate object detection in unconstrained environment, we also use a method of background image update. There are problems in tracking a particular object through a sequence of video. It can't rely only on image processing techniques. Thus we solved these problems using a probabilistic framework. Particle filter has been proven to be a robust algorithm to deal with the nonlinear, non-Gaussian problems. In this paper, the particle filter provides a robust object tracking framework under ambiguity conditions and greatly improved estimation accuracy for complicated tracking problems.