Small dim object tracking using a multi objective particle swarm optimisation technique. Issue 9 (1st September 2015)

• Record Type: Journal Article
• Authors: Ahmadi, Kaveh; Salari, Ezzatollah
• Is Part Of: IET image processing. Volume 9:Issue 9(2015)
• Page Start: 820
• Page End: 826
• Publication Date: 2015-09-01
• Subjects: object tracking -- particle swarm optimisation -- clutter -- target tracking -- iterative methods -- image matching -- image sequences; small dim object tracking -- multiobjective particle swarm optimisation technique -- heavy clutter environment -- image processing -- geometrical information -- multiple hypotheses testing -- MHT -- target tracking system -- tree structure -- iterative process -- image matching -- image sequence data set; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2014.0927
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Dim object tracking in a heavy clutter environment is a theoretical and technological challenge in the field of image processing. For a small dim object, conventional tracking methods fail for the lack of geometrical information. Multiple hypotheses testing (MHT) is one of the generally accepted methods in target tracking systems. However, processing a tree structure with a significant number of branches in MHT has been a challenging issue. Tracking high‐speed objects with traditional MHT requires some presumptions which limit the capabilities of these methods. This study presents a hierarchal tracking system in two levels to solve this problem. For each point in the lower‐level, a multi objective particle swarm optimisation technique is applied to a group of consecutive frames to reduce the number of branches in each tracking tree. Thus, an optimum track for each moving object is obtained in a group of frames. In the upper‐level, an iterative process is used to connect the matching optimum tracks of the consecutive frames based on the spatial information and fitness values. The experimental results show that the proposed method has a superior performance in relation to some common dim object tracking methods over different image sequence data sets.