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In the pedestrian inertial navigation system, the zero velocity detection is the premise to reset the velocity errors and estimate the navigation errors, so it plays an important role in the system. Aiming at the problems of low accuracy and poor robustness in the existing zero velocity detection method, the proposed method adopts an effective gait cycle segmentation method and introduces a Bayesian network (BN) model based on the measurements of inertial sensors and kinesiology knowledge to infer the ZV period. This method can effectively reduce the ambiguity of the zero velocity (ZV) boundaries, and enhance the robustness of the zero velocity detection. The experiments reveal that the removal rate of ZV false detections by zero velocity detection method based on BN increases and the method is effective at high walking speed.