A Cluster Tracking Method Based on Cam-shift

Abstract

The classic Mean-Shift algorithm lacks the necessary template update, because window size remains the same in tracking process, tracking will fail when the template scale change, track will be ineffective when the template is faster, the feature of histogram seems simple in the object color characteristic described aspects and lacks space information. This paper presents a Cam-Shift clustering algorithm, regarding the centroid position of the moving object which is detected as the first iteration of the input frame, narrowing the visual search range, shortening the matching time. Through the template update of color model and cluster, the algorithm resists the interference of light, strain, shelter and achieving more precise moving object motion parameter estimation and tracking results than the existing algorithms to a certain extent. Experiment is used to verify each algorithm in this article. Selecting aerial image sequence multiple moving vehicle object to study, results are capable of getting sustained and effective track to multiple moving objects in image sequences.