Multi-object tracking via MHT with multiple information fusion in surveillance video

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• 作者：Long Ying ; Tianzhu Zhang ; Changsheng Xu
• 关键词：Multi ; object tracking ; Data association ; MHT ; Multiple information fusion
• 刊名：Multimedia Systems
• 出版年：2015
• 出版时间：June 2015
• 年：2015
• 卷：21
• 期：3
• 页码：313-326
• 全文大小：7,822 KB
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• 作者单位：Long Ying (1)
  Tianzhu Zhang (1)
  Changsheng Xu (1)
  
  1. National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems
  Computer Communication Networks
  Operating Systems
  Data Storage Representation
  Data Encryption
  Computer Graphics
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1432-1882

文摘

Tracking multiple objects is critical to automatic video content analysis and virtual reality. The major problem is how to solve data association problem when ambiguous measurements are caused by objects in close proximity. To tackle this problem, we propose a multiple information fusion-based multiple hypotheses tracking algorithm integrated with appearance feature, local motion pattern feature and repulsion–inertia model for multi-object tracking. Appearance model based on HSV–local binary patterns histogram and local motion pattern based on optical flow are adopted to describe objects. A likelihood calculation framework is proposed to incorporate the similarities of appearance, dynamic process and local motion pattern. To consider the changes in appearance and motion pattern over time, we make use of an effective template updating strategy for each object. In addition, a repulsion–inertia model is adopted to explore more useful information from ambiguous detections. Experimental results show that the proposed approach generates better trajectories with less missing objects and identity switches.