抄録
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 specified object tracking with particle filter 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.
