Robust Visual Tracking Revisited: From Correlation Filter to Template Matching

DOI10.1109/TIP.2018.2813161zbMATH Open1409.94367arXiv1904.06842WikidataQ52640738 ScholiaQ52640738MaRDI QIDQ4617276

Author name not available (Why is that?)

Publication date: 6 February 2019

Published in: IEEE Transactions on Image Processing (Search for Journal in Brave)

Abstract: In this paper, we propose a novel matching based tracker by investigating the relationship between template matching and the recent popular correlation filter based trackers (CFTs). Compared to the correlation operation in CFTs, a sophisticated similarity metric termed "mutual buddies similarity" (MBS) is proposed to exploit the relationship of multiple reciprocal nearest neighbors for target matching. By doing so, our tracker obtains powerful discriminative ability on distinguishing target and background as demonstrated by both empirical and theoretical analyses. Besides, instead of utilizing single template with the improper updating scheme in CFTs, we design a novel online template updating strategy named "memory filtering" (MF), which aims to select a certain amount of representative and reliable tracking results in history to construct the current stable and expressive template set. This scheme is beneficial for the proposed tracker to comprehensively "understand" the target appearance variations, "recall" some stable results. Both qualitative and quantitative evaluations on two benchmarks suggest that the proposed tracking method performs favorably against some recently developed CFTs and other competitive trackers.

Full work available at URL: https://arxiv.org/abs/1904.06842

Mathematics Subject Classification ID

Image analysis in multivariate analysis (62H35) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)