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Depth-Based Real-Time Hand Tracking with Occlusion Handling Using Kalman Filter and DAM-Shift

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Computer Vision - ACCV 2014 Workshops (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9008))

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Abstract

In this paper, we propose real-time hand tracking with a depth camera by using a Kalman Filter and an improved DAM-Shift (Depth-based adaptive mean shift) algorithm for occlusion handling. DAM-Shift is a useful algorithm for hand tracking, but difficult to track when occlusion occurs. To detect the hand region, we use a classifier that combines a boosting and a cascade structure. To verify occlusion, we predict in real time the center position of the hand region using Kalman Filter and calculate the major axis using the central moment of the preceding depth image. Using these factors, we measure real-time hand thickness through a projection and the threshold value of the thickness using a 2nd linear model. If the hand region is partially occluded, we cut the useless region. Experimental results show that the proposed approach outperforms the existing method.

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Acknowledgement

This study was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2013R1A1A2012012).

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Authors and Affiliations

  1. School of Media, Soongsil University, Seoul, Korea

    Kisang Kim & Hyung-Il Choi

Authors
  1. Kisang Kim
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  2. Hyung-Il Choi
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Corresponding author

Correspondence to Hyung-Il Choi .

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Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    C.V. Jawahar

  2. Institue of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

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Kim, K., Choi, HI. (2015). Depth-Based Real-Time Hand Tracking with Occlusion Handling Using Kalman Filter and DAM-Shift. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9008. Springer, Cham. https://doi.org/10.1007/978-3-319-16628-5_16

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  • DOI: https://doi.org/10.1007/978-3-319-16628-5_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16627-8

  • Online ISBN: 978-3-319-16628-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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