Optical flow-based observation models for particle filter tracking

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• 作者：Manuel Lucena (1)
  Jose Manuel Fuertes (1)
  Nicolas Perez de la Blanca (2)
  
  1. Department of Computer Science ; Escuela Politecnica Superior ; University of Jaen ; Campus de las Lagunillas ; 23071 ; Ja茅n ; Spain
  2. Department of Computer Science and A.I ; University of Granada ; 18071 ; Granada ; Spain
  
• 关键词：Object Tracking ; Optical Flow ; Particle Filter
• 刊名：Pattern Analysis & Applications
• 出版年：2015
• 出版时间：February 2015
• 年：2015
• 卷：18
• 期：1
• 页码：135-143
• 全文大小：995 KB
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• 刊物类别：Computer Science
• 刊物主题：Pattern Recognition
  
• 出版者：Springer London
• ISSN：1433-755X

文摘

This paper presents three observation models suitable for particle filter tracking, based on the optical flow of the sequence. Modern optical flow computation techniques can obtain in real time very accurate estimates, so we can use it as a source of evidence for higher level image processing. Our image motion-based models are based, respectively, on: a previously computed optical flow field, the image brightness constraint, and similarity measures. They take into account not only the consistency of the measured optical flow with the motion predicted by the model, but also the presence of optical flow discontinuities on the object boundary. Experimental results show that the resulting trackers are comparable to other, state-of-the-art tracking methods. While the model based on similarity measures provides better performance, the optical flow-field-based model is a suitable option when the flow field is available.