Motion Object Tracking Using the CamShift Method with Algorithm Enhancements

This article presents an approach for motion object tracking utilizing the CamShift (Continuously Adaptive Mean Shift) algorithm. To ensure tracking accuracy, we have modified several bugs present in publicly available CamShift implementations. During our experimentation, we observed intriguing phenomena where tracking performance varied under different lighting conditions. Through experimental analysis and data evaluation, we determined this variation correlates with color histogram distributions. To address this issue, we developed an enhanced algorithm that dynamically adapts color histogram distributions based on ambient lighting conditions. Key implementation aspects include: 1) Initializing histogram bins using HSV color space conversion (cv2.cvtColor) for illumination invariance, 2) Implementing adaptive histogram equalization through cv2.normalize with dynamic threshold adjustment, and 3) Incorporating frame-to-frame histogram correlation checks to maintain tracking consistency. Our experimental results demonstrate that the enhanced algorithm exhibits superior robustness and accuracy compared to the original implementation, achieving more reliable performance in practical applications.