NDLI: Visual object tracking via iterative ant particle filtering

Content Provider	IET Digital Library
Author	Wang, Fasheng Wang, Yanbo He, Jianjun Sun, Fuming Li, Xucheng Zhang, Junxing
Abstract	Visual object tracking remains a challenging task in computer vision although important progress has been made in the past decades. Particle filter (PF) is now a standard framework for solving non-linear/non-Gaussian problems, especially in visual object tracking. This study proposes an ant colony optimisation (ACO)-based iterative PF for object tracking. In the proposed method, the basic idea of ACO is used to simulate the behaviour of a particle moving toward the posterior distribution. Such idea is incorporated into the particle filtering framework in order to overcome the well-known particle impoverishment problem. An iterative unscented Kalman filter is used to design a proposal distribution for particle generation in order to generate better predicted sample states. For the likelihood model, the authors adopt the locality sensitive histogram to model the appearance of the target object, which can better handle the illumination variation during tracking. The experimental results demonstrate that the proposed tracker shows better performance than the other tracking methods.
Starting Page	1636
Ending Page	1644
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 8, Jun (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0967
Journal	IET Image Processing
Publisher Date	2020-03-10
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	ACO-based Iterative PF Ant Colony Optimisation Ant Colony Optimisation-based Computer Vision Computer Vision And Image Processing Technique Filtering Method in Signal Processing Illumination Variation Interpolation And Function Approximation Iterative Ant Particle Filtering Iterative Method Iterative Unscented Kalman Filter Kalman Filter Locality Sensitive Histogram Nonlinear Filter Numerical Analysis Object Tracking Optical, Image And Video Signal Processing Optimisation Technique Particle Filter Particle Generation Particle Impoverishment Problem Posterior Distribution Statistics Target Object Visual Object Tracking
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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