The invention relates to the technical field of image processing, and aims to provide a pedestrian flow statistics method based on deep learning and multi-target tracking. The method mainly comprisesthe following steps: S1, shooting a pedestrian monitoring video and reading an image in the video; S2, setting an effective area and a flow count of the image; S3, constructing a pedestrian detectionmodel based on deep learning and training the pedestrian detection model; S4, performing current pedestrian detection to obtain coordinates and image blocks of a current pedestrian frame; S5, trackingthe current pedestrian by using a multi-target tracking algorithm based on deep learning, and generating coordinates of the current pedestrian; S6, generating a moving track of the current pedestrian; S7, judging whether the current pedestrian leaves the effective area or not; If yes, entering the step S8, and if not, entering the step S4; S8, a noise threshold value is selected, and noise judgment is carried out; And S9, deleting the coordinates of the current pedestrian in the continuous video frames. According to the invention, an accurate flow statistics result can be provided in an actual use scene.