Gait Recognition Using Motion Trajectory Analysis

  • Muhammad Hassan Khan
  • Frederic Li
  • Muhammad Shahid Farid
  • Marcin Grzegorzek
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 578)

Abstract

Gait recognition has received significant attention in the recent years due to its applications in numerous fields of computer vision, particularly in automated person identification in visual surveillance and monitoring systems. In this paper, we propose a novel algorithm for gait recognition using spatio-temporal motion characteristics of a person. The proposed algorithm consists of four steps. First, motion features are extracted from video sequence which are used to generate a codebook in the second step. In a third step, the local descriptors are encoded using Fisher vector encoding. Finally, the encoded features are classified using linear Support Vector Machine (SVM). The performance of the proposed algorithm is evaluated and compared with state-of-the-art on two widely used gait databases TUM GAID and CASIA-A. The recognition results demonstrate the effectiveness of the proposed algorithm.

Keywords

Gait recognition Spatiotemporal model Fisher vector encoding Visual surveillance 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Muhammad Hassan Khan
    • 1
  • Frederic Li
    • 1
  • Muhammad Shahid Farid
    • 2
  • Marcin Grzegorzek
    • 3
  1. 1.Research Group of Pattern RecognitionUniversity of SiegenSiegenGermany
  2. 2.College of Information TechnologyUniversity of the PunjabLahorePakistan
  3. 3.Faculty of Informatics and CommunicationUniversity of Economics in KatowiceKatowicePoland

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