# Robust Visual Tracking Revisited: From Correlation Filter to Template Matching

## TL;DR

This paper introduces a new visual tracking method that combines template matching with a novel similarity metric and an online template updating strategy, achieving improved discrimination and robustness over existing correlation filter-based trackers.

## Contribution

It proposes a mutual buddies similarity metric and a memory filtering template update scheme, enhancing target discrimination and stability in visual tracking.

## Key findings

- Outperforms recent correlation filter trackers on benchmarks.
- Demonstrates strong discriminative ability through empirical and theoretical analysis.
- Achieves favorable results in qualitative and quantitative evaluations.

## 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.