Fast action proposals for human action detection and search

In this paper we target at generating generic action pro-posals in unconstrained videos. Each action proposal cor-responds to a temporal series of spatial bounding boxes, i.e., a spatio-temporal video tube, which has a good poten-tial to locate one human action. Assuming each action is performed by a human with meaningful motion, both ap-pearance and motion cues are utilized to measure the ac-tionness of the video tubes. After picking those spatiotem-poral paths of high actionness scores, our action proposal generation is formulated as a maximum set coverage prob-lem, where greedy search is performed to select a set of action proposals that can maximize the overall actionness score. Compared with existing action proposal approaches, our action proposals do not rely on video segmentation and can be generated in nearly real-time. Experimental results on two challenging datasets, MSRII and UCF 101, validate the superior performance of our action proposals as well as competitive results on action detection and search. 1