Hybrid Particle Filter with Color Histogram for Enhanced Robustness in Object Tracking

Lassaad Ayadi, Hatem Ghodbane

Abstract


In this paper, we describe a hybrid method for detecting and tracking moving objects in image sequences. Particle filters and color histograms are combined in the proposed method to address issues with occlusions, lighting variations, and object appearance. The goal of integrating these two techniques is to improve tracking
robustness. Experiments conducted in the OTB 2013 and OTB 2015 databases show that our method, called PFHist, outperforms several existing trackers. It achieves success up to 80% in terms of overlap rate and 94% accuracy in terms of center location error, especially in cases of partial or total occlusions. Moreover, the RGB color space has been shown to be more efficient than the HSV space, and the use of a reduced number of particles (100) allows for better performance while reducing computational cost. Future work will focus on the automatic selection of the optimal color space and on extending the method to multi-object tracking.


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DOI: https://doi.org/10.31449/inf.v49i30.10279

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