Action recognition with improved trajectories


Recently dense trajectories were shown to be an efficient video representation for action recognition and achieved state-of-the-art results on a variety of datasets. This pa-per improves their performance by taking into account cam-era motion to correct them. To estimate camera motion, we match feature points between frames using SURF descrip-tors and dense optical flow, which are shown to be com-plementary. These matches are, then, used to robustly es-timate a homography with RANSAC. Human motion is in general different from camera motion and generates incon-sistent matches. To improve the estimation, a human de-tector is employed to remove these matches. Given the es-timated camera motion, we remove trajectories consistent with it. We also use this estimation to cancel out camera motion from the optical flow. This significantly improves motion-based descriptors, such as HOF and MBH. Experi-mental results on four challenging action datasets (i.e., Hol-lywood2, HMDB51, Olympic Sports and UCF50) signifi-cantly outperform the current state of the art. 1

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