Adaptive Structured Pooling for Action Recognition
Svebor Karaman, Lorenzo Seidenari, Shugao Ma, Alberto Del Bimbo, Stan Sclaroff · 2014
In this paper, we propose an adaptive structured pooling strategy to solve the action recognition problem in videos. Our method aims at individuating several spatio-temporal pooling regions each corresponding to a consistent spatial and temporal subset of the video. Each subset of the video gives a pooling weights map and is represented as a Fisher vector computed from the soft weighted contributions of all dense trajectories evolving in it. We further represent each video through a graph structure, defined over multiple granularities of spatio-temporal subsets. The graph structures extracted from all videos are finally compared with an efficient graph matching kernel. Our approach does not rely on a fixed partitioning of the video. Moreover, the graph structure depicts both spatial and temporal relationships between the spatio-temporal subsets. Experiments on the UCF Sports and the HighFive datasets show performance above the state-of-the-art.