A Novel Real-Time Tracking Algorithm Based on Kernel Density Estimation

Abstract

AbstractThis paper presents a new approach for tracking objects in image sequences which precisely tracks the constant changes of the objects’ size and orientation. Kernel density estimation (KDE) of the object’s color distribution is built from the object region and used to generate a probability map for each incoming frame. Tracking is accomplished by localizing blobs in the maps. Compared with color histograms which are just empirical estimations of the objects’ color distribution, KDE provides much better description of objects’ color than histograms and promise better probability maps. Different from the popular mean shift video tracking methods which determine objects’ size and orientation using predefined parameters, the proposed algorithm calculates objects’ size and orientation from image moments of the search window, rather than trial of discrete parameters. Experiments show that the proposed algorithm achieved much better tracking precision on real video sequences than histogram based mean shift methodsKeywordsKernel Density EstimationVideo TrackingMean Shift