Target Tracking Based on Mean Shift and KALMAN Filter with Kernel Histogram Filtering

Abstract

Visual object tracking is required in many tasks such as video compression, surveillance, automated video analysis, etc. mean shift algorithm is one of popular methods to this task and has some advantages comparing to other tracking methods. This method would not be appropriate in the case of large target appearance changes and occlusion; therefore target model update could actually improve this method. KALMAN filter is a suitable approach to handle model update. We performed mean shift algorithm with model update ability for tracking in this paper and achieve good results.