Detection, recognition and tracking method of boats on lake surface based on vision

Abstract

A method for identifying and tracking boats based on visual lake surface, including a saliency detection step: extracting ROIs in some areas of the lake shoreline for shoreline detection, performing saliency detection on the lake surface area, and obtaining the degree of certainty of the existence of the target; SVM classifier detection Steps: extract the ROI of the lake area, perform SVM classifier detection, and detect the certainty of the existence of the target; target judgment step: use saliency detection and SVM classifier to continuously detect 15 frames of video frames, and comprehensively judge whether the target is detected; determine the target Ship tracking step: if the target is detected, the target is tracked; if the target is not detected, the video frame is re-detected. The invention has high identification and detection accuracy, strong robustness for tracking targets, and combines three methods of saliency detection targets, classifier identification targets and compressed sensing online tracking, and can more accurately realize the detection, identification and tracking of boats on the lake.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a method for detecting, identifying and tracking boats on a lake surface based on vision. Background technique [0002] Object detection, recognition, and tracking are widely used in many aspects of computer vision, such as security surveillance, robot vision systems, and boats on lakes. Various interferences in the process of detection and recognition, such as background interference, occlusion, target shape and illumination changes, are still technical problems that need to be solved. In addition, during the tracking process, when the target moves fast and the target model changes greatly, it is easy to cause the method to fail. [0003] In order to automatically detect and recognize targets and perform target tracking accurately, researchers have proposed many different methods. Based on the binary classification SVM method, it can get much better results than other ...

AI Technical Summary

Problems solved by technology

Based on the binary classification SVM method, it can get much better results than other algorithms on the small sample training set, but it is difficult to adapt to the new environment; and the method of global contrast saliency detection target based on features such as color and brightness is easily affected by complex environments and Therefore, there is an urgent need for a method for target detection, recognition and tracking with good results.

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