Tracking and identification method, device, electronic device, and storage medium for multiple vessel targets

A tracking and identification device for multiple vessel targets, comprising:

• an acquisition module, configured to obtain video surveillance data and initial AIS data;
• a filtering module, configured to filter the initial AIS data to obtain effective AIS data;
• an extraction module, which is used configured to determine the current position of a vessel based on the effective AIS data, to obtain the parameters of a camera based on the video surveillance data, to project the current position of the vessel to an image coordinate system corresponding to the video surveillance data based on the parameters of the camera, combined with a pinhole imaging model, to extract motion characteristics of the effective AIS data in the video surveillance data from projected data, and to obtain a visual motion trajectory of the vessel based on the motion characteristics of the effective AIS data in the video surveillance data;
• a detection module, which is used to input the video surveillance data into a target detection network to obtain target detection boxes corresponding to multiple vessels;
• the target detection network is obtained based on a training of Yolov5-s network;
• a determination module, which is used to determine an occluded area based on the target detection boxes corresponding to the multiple vessels at a previous time, after deleting a part of the target detection boxes corresponding to the multiple vessels at the current time that falls into the occluded area, determining a prediction detection box of the occluded area based on the visual tracking trajectory of the vessel at the previous time, and determining real-time appearance features based on a appearance features of the prediction detection box before occlusion;
• a tracking module, which is used to input the prediction detection box, the real-time appearance features, and the target detection boxes under a non-occlusion state at the current time into a preset DeepSORT algorithm model, and carrying out an anti-occlusion tracking of the vessel based on the preset DeepSORT algorithm model to obtain a visual tracking trajectory of the vessel at the current time;
• a fusion module, which is used to fuse the effective AIS data corresponding to the multiple vessels into the video surveillance data based on the visual motion trajectory and the visual tracking trajectory of the vessel at the current time to determine identities of the multiple vessels;
• wherein, the parameters of the camera include position of the camera, orientation of the camera, horizontal field angle of the camera, vertical field angle of the camera, height of the camera from water surface, and resolution of the video surveillance data taken by the camera.