Publication: Deep Trajectory Based Gait Recognition for Human Re-identification
Issued Date
2019-02-22
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21593450
21593442
21593442
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2-s2.0-85063206612
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Mahidol University
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SCOPUS
Bibliographic Citation
IEEE Region 10 Annual International Conference, Proceedings/TENCON. Vol.2018-October, (2019), 1723-1726
Suggested Citation
Thunwa Sattrupai, Worapan Kusakunniran Deep Trajectory Based Gait Recognition for Human Re-identification. IEEE Region 10 Annual International Conference, Proceedings/TENCON. Vol.2018-October, (2019), 1723-1726. doi:10.1109/TENCON.2018.8650523 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/50646
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Title
Deep Trajectory Based Gait Recognition for Human Re-identification
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Abstract
© 2018 IEEE. The popular techniques of gait recognition rely on the appearance information, such as Gait Energy Image (GEI). However, they need the pre-processing stage of silhouette segmentation in a walking video. This may not be efficient when the complete silhouette could not be obtained under the cluttered walking environment. It is also sensitive to the changes of walking conditions. Thus, this paper comes up with a new solution using the dense trajectory. This technique is commonly used in the action recognition domain. In this paper, it is used to extract the gait information. The key points and their corresponding trajectories are detected. Then, HOG, HOF, MBHx, MBHy and dense trajectory are extracted from each key point as the point descriptor. In the training phase, the bag of word (BoW) are trained using the extracted point descriptors from the training gait videos. Finally, in the testing phase, the BoW is extracted for each gait video, as the gait feature. The experimental result based on the well-known CASIA gait database B shows the promising performance of the proposed method, under various views.