Continuous tracking method suitable for target grabbing of underwater robot

Abstract

The invention relates to a continuous tracking method suitable for target grabbing of an underwater robot, and belongs to the technical field of visual target tracking. The method comprises the stepsof establishing a kernel-related motion model; performing HOG feature extraction; establishing an appearance model; and updating and iterating the model. According to the invention, based on the kernel correlation filtering theory, dense sampling is realized through cyclic displacement of base samples, so that a richer training sample set is extracted, HOG features are extracted, the appearance model of a tracking target is established, a kernel function is introduced, so that the nonlinear regression problem is solved, the calculation efficiency is improved, whether reinitialization trackingis needed or not is judged according to a feedback result, a self-discrimination mechanism based on system confidence is provided, and continuous tracking of the target is realized. According to the method, stable tracking of the underwater target can be guaranteed, shielding and mistaken tracking conditions can be automatically judged, so that re-identification and re-tracking are realized, and continuous tracking and reliable grabbing of the underwater target are further completed.

Description

technical field [0001] The invention relates to a continuous tracking method suitable for underwater robot target grasping, belonging to the technical field of visual target tracking. Background technique [0002] For a long time, fishing operations for sea creatures such as sea cucumbers, scallops, and sea urchins have usually been done manually. Limited by objective conditions such as diving time, diving depth, working conditions, fishing costs, and the susceptibility of divers to occupational diseases, the use of underwater robots can reduce fishing costs and improve operational safety. Over the past two decades, the field of computer vision has grown rapidly. Tracking methods range from the earliest mean shift algorithm, subspace algorithm, etc. to sparse representation theory, and then to the current mainstream correlation filtering algorithm and deep learning algorithm. So far, although the target tracking algorithm has made great progress, there are still challenges...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the current underwater target tracking often causes tracking and grasping failure due to the similarity between the target and the surrounding environment, and the occlusion of the target by the claws or other parts of the robot. A Continuous Tracking Method for Object Grasping

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