A hybrid algorithm based on particle filter and genetic algorithm for target tracking

The particle filter (PF) is an influential instrument for visual tracking: it relies on the Monte Carlo Chain Framework and Bayesian probability that is of tremendous importance for smart monitoring systems. The current study introduces a particle filter based upon genetic resampling. In the suggested method called Reduced Particle Filter based upon Genetic Algorithm (RPFGA), particles with the highest weights are chosen and go through evolution using a GA in the resampling phase of PF algorithm. Moreover, this study aims to introduce the ideas of marking (marking the target by user (observer) in the first frame of a video sequence) and decreasing image size. Applying both ideas leads to reduced number of particles, the processing time of each frame, and the total tracking time. Additionally, the performance of the offered RPFGA method to tackle the occlusion problem is enhanced by the marking idea. According to the results obtained in challenges, such as Occlusions (OCC), deformation (DEF), low resolution (LR), scale variations(SV), Fast Motions (FM), In-Plane Rotation (IPR), Out-Of-Plane Rotation (OPR), Motion Blur (MB), Illumination Variation (IV) and color similarity between the target and the background, and regarding precision and tracking time, the recommended hybrid approach only with a few particles overtakes the generic particle filter, Particle Swarm Optimization particle filter (PSO-PF) and the particle filter based upon improved cuckoo search (ICS-PF). The suggested method can be applied for real time video objects tracking. (C) 2020 Elsevier Ltd. All rights reserved.