Carried Object Detection and Tracking Using Geometric Shape Models and Spatio-temporal Consistency

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• 作者：Aryana Tavanai (19)
  Muralikrishna Sridhar (19)
  Feng Gu (19)
  Anthony G. Cohn (19)
  David C. Hogg (19)
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2013
• 出版时间：2013
• 年：2013
• 卷：7963
• 期：1
• 页码：234-243
• 全文大小：926KB
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• 作者单位：Aryana Tavanai (19)
  Muralikrishna Sridhar (19)
  Feng Gu (19)
  Anthony G. Cohn (19)
  David C. Hogg (19)
  
  19. School of Computing, University of Leeds, Leeds, LS2 9JT, United Kingdom
  

文摘

This paper proposes a novel approach that detects and tracks carried objects by modelling the person-carried object relationship that is characteristic of the carry event. In order to detect a generic class of carried objects, we propose the use of geometric shape models, instead of using pre-trained object class models or solely relying on protrusions. In order to track the carried objects, we propose a novel optimization procedure that combines spatio-temporal consistency characteristic of the carry event, with conventional properties such as appearance and motion smoothness respectively. The proposed approach substantially outperforms a state-of-the-art approach on two challenging datasets PETS2006 and MINDSEYE2012.