  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.
