Intelligent automobile single-target tracking method based on twin network

Abstract

The invention discloses an intelligent automobile single-target tracking method based on a twin network. After a template and an image to be detected are extracted through a twin network formed by a Mish-channel-dark network to obtain feature information corresponding to the template and the image to be detected, then the feature information is input into a similarity judgment branch and a qualityevaluation branch, finally, the similarity is determined through cross-correlation calculation, and template updating is completed. The Mish-channel-park network is composed of a mish convolution module, an m-Residual module, a channel attention module and a structural body. According to the method, the reasoning capability and the anti-interference capability of the tracking algorithm are improved, the channel attention module is added, the tracking precision of the network is remarkably improved, the time for retrieving the object when the target disappears and reappears is shortened, and the practicability of the network is effectively improved. The twin network designed by the invention has better performance, and can achieve higher speed only by lower configuration during deployment.

Description

technical field [0001] The invention belongs to the technical field of intelligent vehicle vision, and in particular relates to a single-target tracking method for intelligent vehicles based on a twin network. Background technique [0002] Object tracking technology refers to the use of the size and position of the target in the initial frame of a given video sequence to determine the size and position of the target in subsequent frames. Through the target tracking technology, the computer can make full use of the acquired data information. Different from object detection, object tracking not only needs to obtain the current position information and classification information of the target, but also needs to analyze whether the current object is the previously determined object according to the relevant algorithm, which also makes this task have an extraordinary research value. Target tracking can be applied to unmanned driving, analyzing the movement of vehicles or pedest...

AI Technical Summary

Problems solved by technology

Among them, the filter class became the mainstream because of its simple structure, but with the deepening of research, the shortcomings of low precision of the filter class tracking method are becoming more and more difficult to solve

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