Integration of Meanshift Algorithm and Kalman Filter for Monocular Single Target Visual Tracking

This article presents a monocular vision-based single target tracking methodology that integrates the Meanshift algorithm with Kalman filtering. The implementation leverages Meanshift for efficient target localization through gradient ascent optimization in feature space, while Kalman filter predicts target motion dynamics to handle occlusions and rapid movements. Key functions include histogram-based target modeling using color distributions, Bhattacharyya coefficient calculation for similarity measurement, and state prediction-correction cycles for motion estimation. This hybrid approach maintains tracking accuracy during complex target maneuvers and adapts to various applications such as video surveillance and autonomous driving systems. We provide detailed explanations of the algorithmic principles, code implementation structure featuring OpenCV functions for image processing and tracking operations, along with experimental results demonstrating performance under different scenarios.