An Experimental Survey on Correlation Filter-based Tracking

TL;DR

This paper reviews the development of correlation filter-based trackers in visual object tracking, providing extensive experimental comparisons, summarizing a general framework, and discussing recent improvements and future directions.

Contribution

It offers a comprehensive survey of 11 CFTs, summarizes their framework, and evaluates their performance, highlighting recent advancements and potential areas for further improvement.

Findings

State-of-the-art performance achieved by recent CFTs like MUSTer and SAMF.

Correlation filter-based trackers are effective in robustness, speed, and accuracy.

Potential improvements include scale estimation, part-based strategies, and long-term tracking integration.

Abstract

Over these years, Correlation Filter-based Trackers (CFTs) have aroused increasing interests in the field of visual object tracking, and have achieved extremely compelling results in different competitions and benchmarks. In this paper, our goal is to review the developments of CFTs with extensive experimental results. 11 trackers are surveyed in our work, based on which a general framework is summarized. Furthermore, we investigate different training schemes for correlation filters, and also discuss various effective improvements that have been made recently. Comprehensive experiments have been conducted to evaluate the effectiveness and efficiency of the surveyed CFTs, and comparisons have been made with other competing trackers. The experimental results have shown that state-of-art performance, in terms of robustness, speed and accuracy, can be achieved by several recent CFTs, such as MUSTer and SAMF. We find that further improvements for correlation filter-based tracking can be made on estimating scales, applying part-based tracking strategy and cooperating with long-term tracking methods.