Combination of Mean Shift of Colour Signature and Optical Flow for Tracking During Foreground and Background Occlusion

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• 关键词：Object tracking ; MeanShift ; Optical flow ; LK ; Occlusion
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9431
• 期：1
• 页码：87-98
• 全文大小：1,496 KB
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• 作者单位：M. Hedayati (17)
  M. J. Cree (17)
  J. Scott (17)
  
  17. School of Engineering, University of Waikato, Hamilton, 3240, New Zealand
  
• 丛书名：Image and Video Technology
• ISBN：978-3-319-29451-3
• 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

This paper proposes a multiple hypothesis tracking for multiple object tracking with moving camera. The proposed model makes use of the stability of sparse optical flow along with the invariant colour property under size and pose variation, by merging the colour property of objects into optical flow tracking. To evaluate the algorithm five different videos are selected from broadcast horse races where each video represents different challenges that present in object tracking literature. A comparison study of the proposed method, with a colour based mean shift tracking proves the significant improvement in accuracy and stability of object tracking.