Comprehensive Learning Particle Swarm Optimizer for Intelligent Visual Tracking

Ashwini, K; Devi, Manju; Nenavath, Hathiram

doi:10.1109/icicv50876.2021.9388501

Cited by 2 publications

(1 citation statement)

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“…The best observation represents the target for the current frame. Furthermore, the PSO was also employed in visual tracking as stochastic optimization [17], [18], it is based on random particles initialization. These particles have two parameters, first the velocity that controls the movement, second the weight value calculated via fitness function.…”

Section: Introductionmentioning

confidence: 99%

Self scale estimation of the tracking window merged with adaptive particle filter tracker

Azdoud

Aouatif²,

Bouchra³

et al. 2023

IJECE

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Tracking a mobile object is one of the important topics in pattern recognition, but style has some obstacles. A Reliable tracking system must adjust their tracking windows in real time according to appearance changes of the tracked object. Furthermore, it has to deal with many challenges when one or multiple objects need to be tracked, for instance when the target is partially or fully occluded, background clutter, or even some target region is blurred. In this paper, we will present a novel approach for a single object tracking that combines particle filter algorithm and kernel distribution that update its tracking window according to object scale changes, whose name is multi-scale adaptive particle filter tracker. We will demonstrate that the use of particle filter combined with kernel distribution inside the resampling process will provide more accurate object localization within a research area. Furthermore, its average error for target localization was significantly lower than 21.37 pixels as the mean value. We have conducted several experiments on real video sequences and compared acquired results to other existing state of the art trackers to demonstrate the effectiveness of the multi-scale adaptive particle filter tracker.

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Section: Introductionmentioning

confidence: 99%