Intelligent visual object tracking with particle filter based on Modified Grey Wolf Optimizer

Narayana, Manjunath; Nenavath, Hathiram; Chavan, Salim; Rao, L. Koteswara

doi:10.1016/j.ijleo.2019.06.013

Cited by 16 publications

(1 citation statement)

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“…"Intelligent visual object tracking with particle filter based on Modified Grey Wolf Optimizer." [69] 2019…”

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confidence: 99%

A Particle Filter based Visual Object Tracking: A Systematic Review of Current Trends and Research Challenges

Awal,

Refat,

Naznin

et al. 2023

IJACSA

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Visual object tracking is a crucial research area in computer vision because it can simulate a dynamic environment with non-linear motions and multi-modal non-Gaussian noises. However, This paper presents an overview of the recent developments in particle filter-based visual object tracking algorithms and discusses the pros and cons of particle filters, respectively. There are presentations of many different methodologies and algorithms in the research literature. The majority of visual object tracking research at present is on particle filters. In addition, the most advanced technique for visual object tracking has also been developed by combining the convolutional neural network (CNN) and the particle filter. The advantage of particle filters is that they can handle nonlinear models and non-Gaussian advancements, sequentially concentrating on the areas of the state space with higher densities, primarily parallelization, and simplicity of implementation. Despite this, it offers a robust framework for visual object tracking because it incorporates uncertainty and outperforms other filters like the Kalman filter, Kernelized correlation filter, optical filter, mean shift filter, and extended Kalman filter in recognition tests. In contrast, this study provided information on various particle filter features and classifiers.

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“…"Intelligent visual object tracking with particle filter based on Modified Grey Wolf Optimizer." [69] 2019…”

mentioning

confidence: 99%