Research On Particle Filter Tracking Algorithm Based On Meanshift Clustering And Optimization

As one of the most important aspects in the Computer Vision filed, visual targettracking which is a popular issue has a broad application in both military and civilian.Tracking is fundamentally filtering and prediction problem. Basic particle filter algorithmwill play a role of framework, which is analyzed exhaustively and thoroughly, includingsome optimization and improvement combined with some practical issues.The theory and feature of basic practical filter and the way it works will be introducedfirstly in practical tracking; then, another classical algorithm—Mean-Shift will also beintroduced, whose particular optimization feature will be used in particle filter algorithm,after which features of color and space will be introduced separately, including their bothextracting and matching in the framework of particle filter; aiming at fast maneuverabilityand severe occlusion, a new quick search method was proposed; finally, the algorithmoptimization and target feature and tracking framework were integrated together, then, theproposed algorithm was obtained, while the effectiveness of our algorithm were verified bysome targeted experiments. The followings are the ways how we did and the achievementswhich we gained.Aiming at the problems of particle degradation and large computation, Mean-Shift wasembedded after observation, particles’ positions and weights were clustered together withadaptive range, while the quality of the target description was better improved, which wasprerequisite for reducing particle quantity. Furthermore the proposed clustering wasobtained from the circle region by using the distance between particles and target centerestimated.The implementation and application of space-based histogram matching algorithm wasstudied. Tracking under the simple scenario by using RGB color space was carried out,against the large computation of spatial histogram, a simplified method was proposedwhich only counted corresponding interval which were both nonzero between candidateand target template.For the fast maneuverability and severe occultation, a new fast search method wasproposed while occultation and lost occurred. Algorithm then entered normal cycle whentarget has been obtained again. Target position would be corrected by Mean-Shift whichwas estimated from particle filter, by using Bhattacharyya Coefficient between it andtemplate, particle number was then regulated. Thus, a new algorithm with adaptive particle amount and resampling range formed. Experiment results showed that the algorithm whichhas been improved had a robust performance towards some environments includingocclusion and target lost with a moderate real-time performance.