Abstract
Aiming at solving the problem of large tracking error in the tracking process of video moving targets with the unscented Kalman filtering method, a particle filter algorithm is proposed to track video moving targets to improve the tracking effect. Particle filtering is the minimum variance estimation of the system state through the posterior probability distribution. The applications of particle filtering and unscented Kalman filtering in tracking video moving target are compared by using Matlab simulation software. The results show that the particle filter has higher accuracy and better tracking performance than the unscented Kalman filter in tracking video moving target.
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