NDLI: Spider monkey optimisation assisted particle filter for robust object tracking

Content Provider	IET Digital Library
Author	Rohilla, Rajesh Sikri, Vanshaj Kapoor, Rajiv
Abstract	Particle filters (PFs) are sequential Monte Carlo methods that use particle representation of state-space model to implement the recursive Bayesian filter for non-linear and non-Gaussian systems. Owing to this property, PFs have been extensively used for object tracking in recent years. Although PFs provide a robust object tracking framework, they suffer from shortcomings. Particle degeneracy and particle impoverishment brought by the resampling step result in abysmal construction of posterior probability density function (PDF) of the state. To overcome these problems, this work amalgamates two characteristics of population-based heuristic optimisation algorithms: exploration and exploitation with PF implementing dynamic resampling method. The aim of optimisation is to distribute particles in high-likelihood area according to the cognitive effect and improve quality of particles, while the objective of dynamic resampling is to maintain diversity in the particle set. This work uses very efficient spider monkey optimisation to achieve this. Furthermore, to test the efficiency of the proposed algorithm, experiments were carried out on one-dimensional state estimation problem, bearing only tracking problem, standard videos and synthesised videos. Metrics obtained show that the proposed algorithm outplays simple PF, particle swarm optimisation based PF, and cuckoo search based PF, and effectively handles different challenges inherent in object tracking.
Starting Page	207
Ending Page	219
Page Count	13
ISSN	17519632
Volume Number	11
e-ISSN	17519640
Issue Number	Issue 3, Apr (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/11/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2016.0201
Journal	IET Computer Vision
Publisher Date	2016-11-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Dynamic Resampling Method Exploitation Exploration Filtering Method in Signal Processing Monte Carlo Method Object Tracking One Dimensional State Estimation Problem Optical, Image And Video Signal Processing Optimisation Optimisation Technique Particle Degeneracy Particle Filter Particle Impoverishment Particle Representation PF Population-based Heuristic Optimisation Algorithm Recursive Bayesian Filter Robust Object Tracking Framework Sequential Monte Carlo Method Spider Monkey Optimisation Assisted Particle Filter State Estimation State Space Method State-Space Model
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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