Running Gait Recognition Using Arm and Leg Swing for Video Person Re-Identification

Master thesis (2020)

Authors

Y.K. Choi Electrical Engineering, Mathematics and Computer Science

Contributors

J.C. van Gemert Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
S. Pintea Pattern Recognition and Bioinformatics - EEMCS (supervisor 2)
S.E. Verwer Cyber Security - EEMCS (supervisor 2)
Y. Napolean Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2020 Yapkan Choi
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Published Date

30-09-2020

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Person re-identification based on appearance is challenging due to varying views and lighting conditions in different cameras, or when multiple persons wear similar clothing styles and color. Considering these challenges, gait patterns provide an alternative to appearance, as gait can be captured from a distance and at a low resolution. In this paper we investigate and evaluate running gait as a unique attribute for video person re-identification in a recreational long-distance running event with 257 participants. We show that running gait recognition achieves competitive performance compared to video-based approaches in the cross-camera retrieval task and that gait and appearance features are complementary to each other. In addition, we compare gait recognition applied to walking and running sequences. An important difference is that we walk with straight arms, but run with bent arms. We propose to use human semantic parsing to create partial gait silhouettes from body parts to find the most discriminative combination. We demonstrate that the arm and leg swing are the most discriminative parts of the running gait. Our proposed method provides better recognition results by removing the torso from the silhouettes and allowing the arm swing to be more visible.