On the basis of the discriminative scale space tracking algorithm, the position correction method and Kalman filtering algorithm are applied to pedestrian tracking in this study. Due to deformation and environmental changes, pedestrians cannot be accurately tracked. To solve the problem, this study makes full use of the advantage of the fhog feature in pedestrian tracking and takes the position calculated by the position filter in the discriminative scale space algorithm as the center. It extracts the fhog feature of pedestrians again and correlates it with the position filter template to correct the pedestrian position. Then, the Kalman filtering algorithm is used to predict and correct the corrected pedestrian position again, and finally, a new position filter template is trained in the twice-corrected position. In this study, the pedestrian data set in OTB-100 is selected to test the performance of the method. The experimental results show that in the original algorithm position, the fhog feature is extracted again for correlation operations to correct the position of pedestrians. At the same time, the Kalman filtering predicts and corrects the corrected position, which can improve the positioning accuracy of pedestrians again.