The goal of this paper is to identify individuals by analyzing their gait. Instead of using binary silhouettes as input data (as done in many previous works) we propose and evaluate the use of motion descriptors based on densely sampled short-term trajectories. We take advantage of state-of-the-art people detectors to define custom spatial configurations of the descriptors around the target person. Thus, obtaining a pyramidal representation of the gait motion. The local motion features (described by the Divergence-Curl-Shear descriptor [1]) extracted on the different spatial areas of the person are combined into a single high-level gait descriptor by using the Fisher Vector encoding [2]. The proposed approach, coined Pyramidal Fisher Motion, is experimentally validated on the recent 'AVA Multiview Gait' dataset [3]. The results show that this new approach achieves promising results in the problem of gait recognition.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Pyramidal Fisher Motion for Multiview Gait Recognition




    Erscheinungsdatum :

    2014-08-01


    Format / Umfang :

    489749 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Motion Cues Analysis for Parkinson Gait Recognition

    Khan, Taha / Westin, Jerker | BASE | 2011

    Freier Zugriff

    Motion cue analysis for parkinsonian gait recognition

    Khan, Taha / Westin, Jerker / Dougherty, Mark | BASE | 2013

    Freier Zugriff

    Motion vector prediction in multiview video coding

    Xun Guo, / Wen Gao, / Debin Zhao, | IEEE | 2005


    Kernelized Multiview Projection for Robust Action Recognition

    Shao, L. / Liu, L. / Yu, M. | British Library Online Contents | 2016


    Optimal motion estimation from multiview normalized epipolar constraint

    Vidal, R. / Ma, Y. / Hsu, S. et al. | IEEE | 2001