Abstract: In the vehicle security inspection system design based on machine vision，a subspace clustering algo-rithm via two granularity optical flow manifold learning was proposed to detect automobile wiper swing angle ampli-tude in order to avoid the defect that the optical flow trajectory is too sparse caused by the complex background of glass． Firstly，the full-length wiper LDOF variational optical flow trajectory was used as coarse-granularity optical flow to perform sparse subspace clustering to obtain reliable seed sample． Then the temporal-spatial similarity to-pography graph of dense and fine granularity optical flow and coarse-granularity optical flow was constructed，and the harmonic function was used to relax the neighbor trajectory label of the seed's to the Gaussian random field for semi-supervised label diffusion to obtain a dense wiper motion region for further RANSAC line fitting and swing an-gle calculation． Finaly，the algorithm module was encapsulated by ocx plugin and embeded into the vehicle track-ing module in the form of callback function for synchronization． Security check video of 153 trains of 4 types of ve-
hicles under 6 different illuminances was collected． The videos was used to analyze and compare the accuracy of fitting and swing angle of two granularity optical flow manifold learning algorithms． The experiment results show that the accuracy of fitting and swing angle of the new algorithm can reach more than 85%，showing a broad appli-cation and promotion prospect．

Key words: machine vision detection, subspace clustering algorithm, dual granularity optical flow manifold learn-ing, variational optical flow, space-time similarity manifold topology, Gaussian random field, harmonic function