Abstract: Aiming at the problems that the existing video association tracking methods could  not accurately extract the association motion trajectory, which led to large deviation in the video association motion  tracking results and low tracking rate, the author proposed a video association motion tracking method based on trajectory extraction algorithm. Firstly, according to the idea of multivariate group, a multivariate group trajectory extraction model was established, the feature distribution vectorization set of moving video image was divided, and the critical value of video image segmentation support vector machine was calculated. Secondly, the pixel features were separated by the color system, and the extracted values of association motion trajectory were output by virtual scene reconstruction. Thirdly, the expected output value was set in the multi granularity filter training, and the Fourier transform was used to transform convolution calculation into point multiplication operation to calculate the minimum rectangular overlap rate of the boundary under each granularity. Finally, the minimum matrix transformation of two boundaries was obtained by Euclidean distance, the trajectory fluctuation degree of each granularity was defined, and the whole process of video association motion tracking was completed. The experimental results show that the video association motion tracking rate of the proposed method is 14.9 frame/s, which can effectively improve the target tracking rate and achieve accurate video association motion tracking.

Key words: trajectory extraction, association motion, video tracking, filter analysis, complex motion