Tracking vehicles as groups in airborne videos

  • Authors:

  • Xianbin Cao;Zhengrong Shi;Pingkun Yan;Xuelong Li

  • Affiliations:

  • School of Electronic Information Engineering, BeiHang University, Beijing 100191, PR China and The Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Acad ...;School of computer Science and Technology, University of Science and Technology of China, Hefei 230026, PR China;Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, ...;Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, ...

  • Venue:
  • Neurocomputing
  • Year:
  • 2013

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Abstract

Airborne vehicle tracking system is receiving increasing attention due to its high mobility, low cost and large surveillance scope. However, tracking multiple vehicles simultaneously on airborne platform is a challenging problem, owing to camera vibration, which causes visible frame-to-frame jitter in the airborne videos and uncertain vehicle motion. To address these problems, a new collaborative tracking framework is proposed in this paper. The framework consists of a two-level tracking process to track vehicles as groups. The higher level builds the relevance network and divides target vehicles into different groups, where the relevance is calculated based on the status information of vehicles obtained from the lower level. The proposed group tracking takes into account the relevance between vehicles and reduces the impact of camera vibration. Experimental results demonstrated that the proposed method has better performance in terms of tracking speed and tracking accuracy compared to other existing approaches based on particle filter and stationary grouping.