This paper proposes a human body detection method based on deep learning and depth of field information. The deep learning method is used for target detection and the depth of field information of the depth map is used to determine the position of the human body, and then the two works are combined to accurately locate the human body. In this method, the RGB image and the corresponding depth map are acquired by the depth camera, and the RGB image is detected by the darknet-yolo-v3. The obtained target bounding box is preprocessed and transmitted to the corresponding depth map of the RGB image, which processes the depth of field information adopting the active contour without edges model and get the aim of combing deep learning with high rate of recognition and depth of field information to accurately locate the target. The experimental results show that this method can accurately find a target positioning point that is not affected by the logo box, effectively improve the problem of increasing the mark box error caused by the different attitude and action amplitude of the human body, improve the accuracy of the detection of human body, and provide a guarantee for further study of pedestrian accurate tracking.