In this paper, we address the problem of searching action proposals in
unconstrained video clips. Our approach starts from actionness estimation on
frame-level bounding boxes, and then aggregates the bounding boxes belonging to
the same actor across frames via linking, associating, tracking to generate
spatial-temporal continuous action paths. To achieve the target, a novel
actionness estimation method is firstly proposed by utilizing both human
appearance and motion cues. Then, the association of the action paths is
formulated as a maximum set coverage problem with the results of actionness
estimation as a priori. To further promote the performance, we design an
improved optimization objective for the problem and provide a greedy search
algorithm to solve it. Finally, a tracking-by-detection scheme is designed to
further refine the searched action paths. Extensive experiments on two
challenging datasets, UCF-Sports and UCF-101, show that the proposed approach
advances state-of-the-art proposal generation performance in terms of both
accuracy and proposal quantity.

本文提出了一个基于动态规划的方法来搜索无限制视频片段的动作建议，通过对每个视频帧上的边界框进行动作度量来实现；提出了一个最大集覆盖问题的解决方案，通过关联行为路径来逐步提高建议的质量。