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Recent works have proposed several long term tracking benchmarks andhighlight the importance of moving towards long-duration tracking to bridge thegap with application requirements. The current evaluation methodologies,however, do not focus on several aspects that are crucial in a long termperspective like Re-detection, Recovery, and Reliability. In this paper, wepropose novel evaluation strategies for a more in-depth analysis of trackersfrom a long-term perspective. More specifically, (a) we test re-detectioncapability of the trackers in the wild by simulating virtual cuts, (b) weinvestigate 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 trackerto track contiguously (without any failure) at a given accuracy. We presentseveral original insights derived from an extensive set of quantitative andqualitative experiments.

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