Learning to Track for Spatio-Temporal Action Localization

Philippe Weinzaepfel, Zaid Harchaoui, Cordelia Schmid; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 3164-3172

Abstract


We propose an effective approach for spatio-temporal action localization in realistic videos. The approach first detects proposals at the frame-level and scores them with a combination of static and motion CNN features. It then tracks high-scoring proposals throughout the video using a tracking-by-detection approach. Our tracker relies simultaneously on instance-level and class-level detectors. The tracks are scored using a spatio-temporal motion histogram, a descriptor at the track level, in combination with the CNN features. Finally, we perform temporal localization of the action using a sliding-window approach at the track level. We present experimental results for spatio-temporal localization on the UCF-Sports, J-HMDB and UCF-101 action localization datasets, where our approach outperforms the state of the art with a margin of 15%, 7% and 12% respectively in mAP.

Related Material


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[bibtex]
@InProceedings{Weinzaepfel_2015_ICCV,
author = {Weinzaepfel, Philippe and Harchaoui, Zaid and Schmid, Cordelia},
title = {Learning to Track for Spatio-Temporal Action Localization},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
}