Mono-camera person tracking based on template matching and covariance descriptor

Abstract

This article presents a simple and efficient approach to persons tracking within large scale environment. The proposed approach is a point matching tracking algorithm based on a covariance descriptor. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of the object and the scene and partial and total occlusions. Tracking is usually performed in the context of higher-level applications that require the location and appearance of the object in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. The ultimate purpose of the proposed approach is to propose an efficient tracking algorithm as a way for real time multi-shot re-identification. This approach is evaluated using standard datasets.