Sea surface ship intelligent tracking system and method based on machine vision

Abstract

The present invention provides a sea surface ship intelligent tracking system and method based on machine vision. The system comprises an image collection module, a Mean-shift tracking module, a Kalman filter module, a multi-stage parallel detection module and online learning module. The sea surface ship intelligent tracking system and method based on the machine vision employs a Haar+AdaBoost combination algorithm to detect the sea surface ships, and the detection result is taken as the starting frame of the tracker so as to initialize the tracker and the detector and replace the manual delineation of a target area. The Kalman filter is configured for the detection and the tracking of the ship so as to shorten the detection range, reduce the calculation amount and improve the timeliness. Based on the Mean-shift tracking module, the stable feature, anti-shielding, simple calculation and insensitivity of target deformation, rotation and background movement; the multi-stage parallel detection module is employed to combine the set classifier based on the random forest, the Haar classifier and the nearest neighbor classifier so as to solve the difficulty that the ship tracking is failed caused by shielding and deformation and improve the tracking timeliness, the accuracy and the robustness.

Description

technical field [0001] The invention belongs to the technical field of computer vision and artificial intelligence, and relates to the intelligent tracking technology of sea vessels, in particular to a machine vision-based intelligent tracking system and method for sea vessels. Background technique [0002] The ocean is of great strategic significance to the economic and social development of a country, especially for countries with vast sea areas and long coastlines. Performing missions at sea presents some unique challenges for surface unmanned aerial vehicles compared to land or air domains. Due to the complex sea surface environment, it is in the process of completing the missions of environmental detection, target recognition, and autonomous obstacle avoidance. Maritime unmanned aerial vehicles must not only maintain a certain safe distance from the land, but also must maintain a safe distance from floating and fixed objects on the route, which puts forward higher requ...

AI Technical Summary

Problems solved by technology

However, this system has a fatal flaw: when the tracked target has shape changes or occlusions, the tracking is easy to fail; therefore, for long-term tracking or tracking when the tracked target has a shape change method instead of tracking

In other words, although the detection method can achieve the purpose of long-term tracking, it has high requirements for the selection of training samples, otherwise, it is difficult to ensure the robustness of tracking

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