Multi-Point Visual Automatic Tracking and Imitation of Human Upper Limb Movement Posture
The noise inherent in visual sensors and motion blur results in noisy and erroneous data when captu-ring the upper limb movement poses,as well as lower accuracy in multi-point visual automatic tracking.Therefore,a multi-point visual automatic tracking method for human upper limb movement postures was proposed.Firstly,multi-dimensional sensors were deployed to collect the postures from multiple points.Then,a sensor acceleration error model was built to obtain an objective function of error correction.Moreover,the Ant Colony Optimization(ACO)al-gorithm was utilized for optimization,thus correcting the errors in the acquisition process of sensors.Finally,the Kal-man Filtering Algorithm(Kalman)was employed to achieve the multi-point visual automatic tracking of human upper limb movement poses.Experimental results show that the proposed method can improve the accuracy and efficiency of pose tracking while ensuring tracking stability.The tracking time is only around 10ms.
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