Provided in the present disclosure is a multimodal unsupervised pedestrian pixel-level semantic labeling method and system, the method comprising: performing three-dimensional reconstruction on an unmanned monitoring scene, and acquiring initial point cloud information of the monitoring scene; acquiring first point cloud information in the monitoring scene by using a Tof image acquisition device, registering the first point cloud information with the initial point cloud information and then executing a set difference operation to acquire second point cloud information, and projecting the second point cloud information on a horizontal plane to obtain a personnel point cloud information set; expanding and corroding a binary image after thresholding scene information acquired by an infrared image acquisition device to obtain a connected area information set; using positional relationships between calibrated cameras to project the personnel point cloud information set and the connected area information set into an image plane space of an RGB image acquisition device so as to perform a set intersection operation, and acquiring a corresponding human body area set in response to when a common pixel exceeds a first threshold. The method and system fully integrate advantages of cameras having different modalities, and can effectively extract human pixel points in the scene.
MULTIMODAL UNSUPERVISED PEDESTRIAN PIXEL-LEVEL SEMANTIC LABELING METHOD AND SYSTEM