Object Tracking Using 2-D FFT with Enhanced ROI and CLAHE Adjustments

📜 ABSTRACT

This study extends the capabilities of a 2-D FFT-based object tracking algorithm to dynamically optimize Region of Interest (ROI) and improve edge detection accuracy by integrating Contrast Limited Adaptive Histogram Equalization (CLAHE). The improvements address issues with edge detection noise, especially from background elements like clouds, and enhance the robustness of object tracking. Adjustments include restructuring workspace handling, eliminating redundant assignments, and refining the initialization process

📚 Referanslar

1. Object Tracking Using 2-D FFT
2. D. S. Bolme, J. R. Beveridge, B. A. Draper and Y. M. Lui, "Visual object tracking using adaptive correlation filters," 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, pp. 2544–2550, doi: 10.1109/CVPR.2010.5539960.