  Compared with short-term tracking, the long-term tracking task requires
determining the tracked object is present or absent, and then estimating the
accurate bounding box if present or conducting image-wide re-detection if
absent. Until now, few attempts have been done although this task is much
closer to designing practical tracking systems. In this work, we propose a
novel long-term tracking framework based on deep regression and verification
networks. The offline-trained regression model is designed using the
object-aware feature fusion and region proposal networks to generate a series
of candidates and estimate their similarity scores effectively. The
verification network evaluates these candidates to output the optimal one as
the tracked object with its classification score, which is online updated to
adapt to the appearance variations based on newly reliable observations. The
similarity and classification scores are combined to obtain a final confidence
value, based on which our tracker can determine the absence of the target
accurately and conduct image-wide re-detection to capture the target
successfully when it reappears. Extensive experiments show that our tracker
achieves the best performance on the VOT2018 long-term challenge and
state-of-the-art results on the OxUvA long-term dataset.
