Action Tubelet Detector for Spatio-Temporal Action Localization

Vicky Kalogeiton, Philippe Weinzaepfel, Vittorio Ferrari, Cordelia Schmid

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Current state-of-the-art approaches for spatio-temporal action localization rely on detections at the frame level that are then linked or tracked across time. In this paper, we leverage the temporal continuity of videos instead of operating at the frame level. We propose the ACtion Tubelet detector (ACT-detector) that takes as input a sequence of frames and outputs tubelets, i.e., sequences of bounding boxes with associated scores. The same way state-of-the art object detectors rely on anchor boxes, our ACT-detector is based on anchor cuboids. We build upon the SSD framework [19]. Convolutional features are extracted for each frame, while scores and regressions are based on the temporal stacking of these features, thus exploiting information from a sequence. Our experimental results show that leveraging sequences of frames significantly improves detection performance over using individual frames. The gain of our tubelet detector can be explained by both more accurate scores and more precise localization. Our ACT-detector outperforms the state-of-the-art methods for frame-mAP and video-mAP on the J-HMDB [12] and UCF-101 [31] datasets, in particular at high overlap thresholds.
Original languageEnglish
Title of host publicationInternational Conference on Computer Vision (ICCV 2017)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages9
ISBN (Electronic)978-1-5386-1032-9
ISBN (Print)978-1-5386-1033-6
Publication statusPublished - 25 Dec 2017
Event2017 IEEE International Conference on Computer Vision - Venice, Italy
Duration: 22 Oct 201729 Oct 2017

Publication series

ISSN (Electronic)2380-7504


Conference2017 IEEE International Conference on Computer Vision
Abbreviated titleICCV 2017
Internet address


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