A real-time visual object tracking system based on Kalman filter and MB-LBP feature matching

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• 作者：Zebin Cai ; Zhenghui Gu ; Zhu Liang Yu ; Hao Liu…
• 关键词：Object tracking ; Feature matching ; Kalman filter ; Multi ; scale block local binary patterns
• 刊名：Multimedia Tools and Applications
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：75
• 期：4
• 页码：2393-2409
• 全文大小：2,962 KB
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• 作者单位：Zebin Cai (1)
  Zhenghui Gu (1)
  Zhu Liang Yu (1)
  Hao Liu (2)
  Ke Zhang (3)
  
  1. College of Automation Science and Engineering, South China University of Technology, Guangzhou, China
  2. Beijing Transportation Information Center, Beijing, China
  3. Beijing Transportation Operation Coordination Center, Beijing, China
  
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems
  Computer Communication Networks
  Data Structures, Cryptology and Information Theory
  Special Purpose and Application-Based Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7721

文摘

Visual tracking has very important applications in practice. Many proposed visual trackers are not suitable for real-time applications because of their huge computational loads or sensitivities against changing environments such as illumination variation. In this paper, we propose a new tracker which uses modified Multi-scale Block Local Binary Patterns (MB-LBP) like feature to characterize the tracked object. Such feature has low computational load and robustness against illumination variation. An updated appearance model is build based on the modified MB-LBP feature. The model is updated in every frame by replacing the appearance model with the features extracted from the most current detected image patch of target. Moreover, we use the predicted information about the target to constructed a smaller searching area for target in new frame. It greatly reduces computational load for target searching. Numerical experiments show that the drift effect of tracker is greatly avoided and the tracker has very effective and robust performance on various test videos.