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A Study Of Mean Shift And Correlative Algorithm In Visual Tracking

Posted on:2007-11-14Degree:DoctorType:Dissertation
Country:ChinaCandidate:S L ZhuFull Text:PDF
GTID:1118360182486816Subject:Control theory and control engineering
Abstract/Summary:PDF Full Text Request
Mean Shift is a very good algorithm in visual target tracking area. Many scholars in foreign countries has developed this algorithm in recent years, however, few scholars study it in our country. A few articles can be found last year ever.I experienced many difficulties, when I entered the realm of target tracking. Finally I found the Mean Shift algorithm. I have spent many times in this algorithm, so my dissertation is named Mean Shift. The algorithm has many advantages, for example: very good real-time, robust for occlusion and target distortion, however, it has a few defects. Many improvements are done for these defects. My dissertation is named Mean Shift although, but the content is out the scope of Mean Shift.In third chapter, an algorithm combining kalman filter and Mean Shift is bring forward, arm at the Mean Shift couldn't tracking the fast moving target. Kalman filter is used to forecast possible position of target, then Mean Shift search the real position near the possible position. The algorithm has good effect to fast moving target, and can deal well to occlution.In fourth chapter, a template update algorithm of Mean Shift is put forward. Template update is very important to target tracking. There isn't general algorithm for it however, many template update is arm to given tracking algorithm. In this chapter, an algorithm of template update of Mean Shift based a group kalman filter is proposed. The element of template is probability of eigenvalue of target. These probability are acquire by a kalman filter group which had 48 kalman filters. The whole algorithm conformation is very skilled. Because the number of kalman filter is lesser, algorithm is real-time, and robust.In fifth chapter, an algorithm based on kernel histogram particle filter proposed. System dynamic model of algorithm can learn the velocity of target, so the dimension of particle is reduced and the required particle is very few. Observation model of algorithm is based on Mean Shift describing the eigenvalue. In this chapter, a new template update algorithm is devised. New algorithm makes the best of middle value of particle filter, so that thecomplexity of algorithm is not added. Template update enable to get more credible observation, so improve the robustness of algorithm.In sixth chapter, an algorithm based on Mean Shift particle filter is proposed. The main disadvantage of particle filter is it require so many particles to approximately describing state of target, that the real-time of algorithm is not ensured. New algorithm use Mean Shift algorithm to muster the particle to area of real state after re-sample .Because the particle describe the state of more rationally , the needed particle is reduced, and the real-time of algorithm is improved. In this chapter, in the basis of kernel particle filter, the new algorithm could adjust number of particle adaptively, the agility of whole algorithm is better. Experiment result show algorithm could tracking target very well when target is occluded, and algorithm is more real-time comparing with traditional particle filter...
Keywords/Search Tags:Visual target tracking, Mean Shift, kernel function, particle filter, kalamn filter, kernel particle filter
PDF Full Text Request
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