A multi view gait cycle detection method fitting the geometric features of lower limbs is proposed to address the issue of existing gait cycle detection methods being susceptible to changes in shooting angles. Firstly， the human posture topology in the gait video sequence was extracted by the MediaPipe model， simplifying the image preprocessing process. Then， by analyzing the periodic dynamic change law between the joint points in the human posture topology map under walking state， the inclination formed by the left shin and the horizontal ground and the Euclidean distance ratio from the midpoint of the left and right hip joints to the left and right ankle are extracted as features. Finally， the feature data were fitted into sinusoidal function waves by Fourier transform， and the gait period is detected based on the fitting results. Compared with the current mainstream gait cycle detection methods， the proposed method has achieved good front and back view and strabismus angle detection results.