A color-based particle filter optimized using firefly and black hole algorithms for moving object tracking

Devira Anggi Maharani; Lenni Yulianti; Carmadi Machbub; Pranoto Hidaya Rusmin; Suci Aulia; Rizki Dwi Romadhona

doi:10.33795/eltek.v24i2.9879

Authors

Devira Anggi Maharani Politeknik Negeri Malang
Lenni Yulianti Institut Teknologi Bandung
Carmadi Machbub Institut Teknologi Sains Bandung
Pranoto Hidaya Rusmin Institut Teknologi Bandung
Suci Aulia Telkom University
Rizki Dwi Romadhona Politeknik Negeri Malang

DOI:

https://doi.org/10.33795/eltek.v24i2.9879

Keywords:

tracker, black hole, firefly, particle filter, moving object tracking, PTZ Camera

Abstract

Many researchers, particularly in the field of computer vision, have increasingly focused on moving object tracking. However, accurately tracking a moving object remains challenging, especially under conditions such as occlusion, illumination changes, abrupt motion, and variations in object posture. Although recent developments have enhanced the performance of visual object tracking methods, the task remains challenging under real-world conditions. Particle Filter (PF), a Monte Carlo-based estimation framework, has been extensively applied to visual object tracking due to its ability to handle nonlinear and non-Gaussian systems. However, conventional PF methods still suffer from particle degeneracy and sample impoverishment, particularly after repeated resampling processes. Although resampling can reduce degeneracy by removing particles with insignificant weights, it may also decrease particle diversity and degrade tracking performance. To address these limitations, this study introduces a Black Hole Optimization Particle Filter (BHOPF), in which the Black Hole Optimization algorithm is incorporated into the PF resampling process. The proposed approach updates particles through the Black Hole optimization mechanism, offering a simple computational structure with minimal parameter tuning. The integration of Black Hole Optimization is expected to improve particle diversity, increase the effective number of particles, reduce RMSE, and enhance the robustness of PF-based visual object tracking. The experimental results show that, using 30 particles and 5 iterations, BHOPF increased the effective number of particles to 26. In comparison, the Firefly Optimization Particle Filter (FAOPF) achieved 23 effective particles, while the conventional Particle Filter achieved only 16 effective particles. The proposed system achieved real-time operation through multithreading and pipeline-based processing, with a processing speed of up to 30 FPS.

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