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

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• 作者：Kisang Kim (15)
  Hyung-Il Choi (15)
  
  15. School of Media ; Soongsil University ; Seoul ; Korea
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9008
• 期：1
• 页码：218-226
• 全文大小：3,247 KB
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• 作者单位：Computer Vision - ACCV 2014 Workshops
• 丛书名：978-3-319-16627-8
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

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.