  Deep learning based visual trackers entail offline pre-training on large
volumes of video datasets with accurate bounding box annotations that are
labor-expensive to achieve. We present a new framework to facilitate bounding
box annotations for video sequences, which investigates a
selection-and-refinement strategy to automatically improve the preliminary
annotations generated by tracking algorithms. A temporal assessment network
(T-Assess Net) is proposed which is able to capture the temporal coherence of
target locations and select reliable tracking results by measuring their
quality. Meanwhile, a visual-geometry refinement network (VG-Refine Net) is
also designed to further enhance the selected tracking results by considering
both target appearance and temporal geometry constraints, allowing inaccurate
tracking results to be corrected. The combination of the above two networks
provides a principled approach to ensure the quality of automatic video
annotation. Experiments on large scale tracking benchmarks demonstrate that our
method can deliver highly accurate bounding box annotations and significantly
reduce human labor by 94.0%, yielding an effective means to further boost
tracking performance with augmented training data.
