Object Tracking with Improved Detector of Objects Similar to Target

Abstract

Abstract Tracking methods based on the particle filter uses frequently the appearance information of the target object to calculate the likelihood. The method using it often fails in tracking when the target object intersects with objects similar to the target object. We propose a new approach for tracking an object in a video sequence taken by a moving camera. The proposed method is based on the particle filter. During tracking the target object, the method detects a similar object near the target object by the Mean-Shift tracker. After detecting the object, the size of it is recalculated and the similar object is tracked by the same way with the target object. The positions of the similar objects are used for calculating the likelihood and for judging situation under which the target object exists. These prevent the method from tracking the other object mistakenly. Results are demonstrated by experiments using real video sequences.