Open AccessSmall dim object tracking using a multi objective particle swarm optimisation technique
Author(s) -
Ahmadi Kaveh,
Salari Ezzatollah
Publication year - 2015
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2014.0927
Subject(s) - tracking (education) , clutter , computer vision , video tracking , artificial intelligence , particle swarm optimization , computer science , object (grammar) , point set registration , tree (set theory) , tracking system , process (computing) , matching (statistics) , object detection , tree structure , point (geometry) , pattern recognition (psychology) , mathematics , algorithm , kalman filter , radar , psychology , pedagogy , telecommunications , mathematical analysis , statistics , geometry , binary tree , operating system
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.