Exploring 3 R's of Long-term Tracking: Re-detection, Recovery and Reliability

TL;DR

This paper introduces new evaluation strategies for long-term visual tracking, focusing on re-detection, recovery, and reliability, providing deeper insights into tracker performance over extended durations.

Contribution

It proposes novel metrics and testing methods to assess long-term tracking capabilities, addressing gaps in current evaluation methodologies.

Findings

Trackers' re-detection in wild conditions is effectively tested using simulated cuts.

Chance plays a significant role in tracker recovery after failure.

A new metric enables visual assessment of continuous tracking accuracy without failures.

Abstract

Recent works have proposed several long term tracking benchmarks and highlight the importance of moving towards long-duration tracking to bridge the gap with application requirements. The current evaluation methodologies, however, do not focus on several aspects that are crucial in a long term perspective like Re-detection, Recovery, and Reliability. In this paper, we propose novel evaluation strategies for a more in-depth analysis of trackers from a long-term perspective. More specifically, (a) we test re-detection capability of the trackers in the wild by simulating virtual cuts, (b) we investigate the role of chance in the recovery of tracker after failure and (c) we propose a novel metric allowing visual inference on the ability of a tracker to track contiguously (without any failure) at a given accuracy. We present several original insights derived from an extensive set of quantitative and qualitative experiments.