Detection and tracking of multiple similar objects based on color-pattern

Abstract

In this paper an efficient and applicable approach for tracking multiple similar objects in dynamic environments is proposed. Objects are detected based on a specific color pattern i.e. color label. It is assumed that the number of objects is not fixed and they can be occluded by other objects. Considering the detected objects, an efficient algorithm to solve the multi-frame object correspondence problem is presented. The proposed algorithm is divided into two steps; at the first step, previous mismatched correspondences are corrected using the new information (i.e. new detected objects in new image frame), then all tail objects (i.e. objects which are located at the end of a track) are tried to be matched with unmatched objects (either a new object or a previously mismatched object). Apart from the correspondence algorithm, a probabilistic gain function is used to specify the matching weight between objects in consecutive frames. This gain function benefits Student T distribution function for comparing different object feature vectors. The result of the algorithm on real data shows the efficiency and reliability of the proposed method. © 2011 Springer-Verlag.