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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2020

02.08.2019

Action Recognition with Multiple Relative Descriptors of Trajectories

verfasst von: Zhongke Liao, Haifeng Hu, Yichu Liu

Erschienen in: Neural Processing Letters | Ausgabe 1/2020

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Abstract

Dense trajectory has become one of the most successful hand-crafted features for action recognition. However, most of the existing dense trajectories based methods ignore the relationship between trajectories. In this paper, we propose multiple relative descriptors of trajectories to model the relative information of pairs of trajectories. Specifically, we present relative motion descriptors and relative location descriptors, which are utilized to capture the relative motion information and relative location information respectively. Moreover, we present relative deep feature descriptors which combine the deep features with hand-crafted features. By aggregating the above descriptors, we obtain the fixed-length representation regardless of the various duration of input video. The experimental results on three standard datasets demonstrate the superiority of our method.
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Metadaten
Titel
Action Recognition with Multiple Relative Descriptors of Trajectories
verfasst von
Zhongke Liao
Haifeng Hu
Yichu Liu
Publikationsdatum
02.08.2019
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2020
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-019-10091-z

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