  Existing tracking methods mainly focus on learning better target
representation or developing more robust prediction models to improve tracking
performance. While tracking performance has significantly improved, the target
loss issue occurs frequently due to tracking failures, complete occlusion, or
out-of-view situations. However, considerably less attention is paid to the
self-recovery issue of tracking methods, which is crucial for practical
applications. To this end, we propose a recoverable tracking framework,
RTracker, that uses a tree-structured memory to dynamically associate a tracker
and a detector to enable self-recovery ability. Specifically, we propose a
Positive-Negative Tree-structured memory to chronologically store and maintain
positive and negative target samples. Upon the PN tree memory, we develop
corresponding walking rules for determining the state of the target and define
a set of control flows to unite the tracker and the detector in different
tracking scenarios. Our core idea is to use the support samples of positive and
negative target categories to establish a relative distance-based criterion for
a reliable assessment of target loss. The favorable performance in comparison
against the state-of-the-art methods on numerous challenging benchmarks
demonstrates the effectiveness of the proposed algorithm.
