Abstract
ABSTRACTA multi-frame motion video detection algorithm of Gaussian Monte Carlo particle filter is proposed to improve the applicability of video anomaly detection algorithm and enhance the recognition efficiency and accuracy of the algorithm. Firstly, the segment sequence is divided for multi-day video frames and then the total number of optical flow vectors is counted and the feature vectors are extracted for the pixel positions of all frames. Secondly, a particle filter algorithm is introduced to construct a maneuvering target tracking model to achieve effective tracking of maneuvering target, and at the same time, Gaussian and Monte Carlo methods are used to improve the particle resampling extraction process to enhance the particle extraction efficiency and performance so as to improve the performance of the particle filter algorithm; Finally, the experimental comparison on the maneuvering target tracking model shows that the proposed algorithm is superior to the compared algorithm in the tracking accuracy and tracking efficiency, reflecting the effectiveness of the proposed algorithm.
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