Scale adaptive region covariance descriptor for visual tracking

Abstract

This paper presents an adaptive approach for scale estimation in a tracking-by-detection framework. The proposed method works by learning covariance descriptor based on multi-layer instance search region. Our results show that the proposed approach significantly improves the performance in term of detection rate compared to region covariance descriptor with using a fixed bounding box (single scale). From this work, it is believed that we have constructed a greater solution in choosing best layer for this descriptor, permitting to move forward to the next issues such as fast motion or motion blur for achieving a robust tracking system.