Abstract
An improved Mean Shift target tracking algorithm integrated with the Kalman Filter (KFMS algorithm) is proposed to address the challenge of accurately tracking targets in complex environments using parallel robots. This algorithm combines the local search capability of the Mean Shift algorithm with the state estimation capability of the Kalman Filter, reducing the impact of complex environments on tracking performance. It achieves accurate global tracking of targets by the camera, effectively improving the stability and accuracy of dynamic target tracking. Experimental results with a local camera show that the target tracking accuracy and success rate of the improved KFMS algorithm are increased by 21.3% and 29.6%, respectively, compared to the Mean Shift algorithm, further validating the effectiveness of the enhanced algorithm.
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