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Learning to track for spatio-temporal action localization


We propose an effective approach for action localization, both in the spatial and temporal domains, in realistic videos. The approach starts from detecting proposals at frame-level, and proceeds to scoring them using a combination of static and motion state-of-the-art features extracted from CNNs. We then track a selection of proposals throughout the video, using a tracking-by-detection approach that leverages a combination of instance-level and class-specific learned detectors. The tracks are scored using a spatio-temporal motion histogram (STMH), a novel descriptor at the track level, in combination with the CNN features. Finally, we perform temporal localization of the action using a sliding-window approach. We present experimental results on the UCF-Sports and J-HMDB action localization datasets, where our approach outperforms the state of the art with a margin of 15% and 7% respectively in mAP. Furthermore, we present the first experimental results on the challenging UCF-101 localization dataset with 24 classes, where we also obtain a promising performance.
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Dates and versions

hal-01159941 , version 1 (05-06-2015)
hal-01159941 , version 2 (01-10-2015)



Philippe Weinzaepfel, Zaid Harchaoui, Cordelia Schmid. Learning to track for spatio-temporal action localization. ICCV - IEEE International Conference on Computer Vision, Dec 2015, Santiago, Chile. pp.3164-3172, ⟨10.1109/ICCV.2015.362⟩. ⟨hal-01159941v2⟩
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