Research On Object Tracking Algorithms In Image Sequence

Target tracking has been one of the focuses and difficulties in computer vision. Due to the illumination changing, background clutter, view changing, occlusion and deformation, at the present stage, most of these the tracking algorithms can not meet the requirement of application. However, the object tracking is the base of the high level computer vision task, such as abnormal behavior like crossing detection, people counting in public places and cars counting on the roads, event analysis and event understand. Therefore, research on this issue has important theoretical significance and practical significance.In this paper, we start our research beginning with the mean shift object tracking algorithm as well as these tracking approaches based on the discriminative object model The mean shift object tracking is a simple and effective algorithm which has been studied by lots of researchers. The tracking methods based on discriminative model have become a hot issue due to the development of the machine learning technology. The major results of our research are as follow:Firstly, we proposed an improved generative tracking algorithm based on the back-ground weighted histogram and the motion direction information. Since the object rect-angle used in the mean shift object tracking maybe include some background pixels, this paper utilizes the background weighted histogram to reduce the influence of the background information. We analyze its principle at details. Based on the analysis, we estimate the motion direction of the object using the motion filters. After that the motion direction in-formation is embedded into the mean shift tracking algorithm to improve the performance of the tracking approach. In addition, we propose a new method for model updating in or-der to track the objects whose appearance change continually. As to the background model updating, we utilize not only the background information, but also we make full use of the target object information. The experiment results show that our proposed method improve the performance of the original method.Secondly, based on the spatial information and model updating we proposed a discrim- inative object tracking algorithm. In the tracking algorithm based on bag of patches, in consideration of the spatial position dependence between these small image patches and the target object as well as the spatial context between these small images patches. We make full use of them when we build the appearance model and construct the confidence map so that the appearance is more scientific and reasonable and the confidence map is more accurate. In addition, a new appearance model updating strategy is proposed to adapt to the variance of the object appearance. The experiment results show that the improved tracking method enhances the performance in accuracy and stability. Comparing with the generative method, the discriminative method does better in distinguishing the object from the background.After that, we proposed a discriminative mean shift tracking method based on the good performance of the discriminative method in distinguishing the target object from the back-ground. We embed the discriminative model into the mean shift tracking framework and make use of its discriminative ability to separate the object from the background. In ad-dition, as to the scale change during the process, we propose a scale adaptive tracking al-gorithm based on random ferns classifiers as well as generative and discriminative model. The compared experiment results illustrate that the proposed method improves the ability of tracking object with scale change.Finally, we summarize our work in this paper, present its innovative points, and then we discuss the future work and research.