A hybrid approach for robust object tracking using particle filters and color histograms
Lassaad Ayadi, Hatem Ghodbane · STUDIES IN ENGINEERING AND EXACT SCIENCES · 2024
This paper proposes a hybrid method for detecting and tracking moving objects in a sequence of images. The approach leverages the advantages of two methods, particle filters and color histograms, in order to address challenges such as false detection, occlusion, and variations in object appearance. By integrating these two methods, we aim to improve robustness against issues like partial or total occlusion, objects with multiple colors, objects of same color that are very close and similar colors between objects and backgrounds. Experimental results on the OTB 2013 and OTB 2015 databases demonstrate that our hybrid method, PFHist, outperforms other trackers in terms of overlap ratio and success rate, especially in handling partial and total occlusion scenarios. The RGB color space proves to be more effective, and using 100 particles generally yields optimal results. Future work includes enhancing the tracker to automatically select the most suitable color space and to track multiple objects.