NDLI: Small dim object tracking using a multi objective particle swarm optimisation technique

Content Provider	IET Digital Library
Author	Ahmadi, Kaveh Salari, Ezzatollah
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.
Starting Page	820
Ending Page	826
Page Count	7
ISSN	17519659
Volume Number	9
e-ISSN	17519667
Issue Number	Issue 9, Sep (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/9/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2014.0927
Journal	IET Image Processing
Publisher Date	2015-09-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Clutter Computer Vision And Image Processing Technique Geometrical Information Heavy Clutter Environment Image Matching Image Processing Image Recognition Image Sequence Image Sequence Data Set Interpolation And Function Approximation Iterative Method Iterative Process MHT Multiobjective Particle Swarm Optimisation Technique Multiple Hypotheses Testing Numerical Analysis Object Tracking Optimisation Technique Particle Swarm Optimisation Small Dim Object Tracking Target Tracking Target Tracking System Tree Structure
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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