Target tracking method based on language modeling twin network

Abstract

The invention relates to a target tracking method based on a language building mode twin network, and the method comprises the steps: S1, obtaining a video containing the continuous motion of a target, and making a training data set according to the video; s2, training a twin neural network according to the training data set; s3, keeping parameters of the twin neural network unchanged, and training a target position extraction network; s4, performing joint training on the trained twin neural network and the trained target position extraction network to obtain a language building mode twin network; and S5, acquiring a real-time image of the target to be tracked, and performing real-time tracking on the target to be tracked by using the language modeling twin network. Expert experience knowledge does not need to be integrated into the algorithm, and the implementation process is simpler and more convenient. In addition, the system is high in expansibility and can be connected to a more general intelligent system.

Description

technical field [0001] The invention relates to the technical field of video target tracking, and more particularly to a target tracking method based on a language-building pattern twin network. Background technique [0002] Target tracking technology has a wide range of application values ​​in both civilian and national defense fields, and is of great significance to the development of robots, aircraft, unmanned vehicles, security and other fields. For example, in the field of security, cameras track pedestrians in the field of view, and after a series of subsequent intelligent algorithm analysis and processing, the monitoring system has a better perception and understanding of human posture, actions, and behavioral intentions, so as to achieve intelligent , timely and efficient monitoring. Auto-following refers to selecting the tracking target in the initialization screen, then tracking the tracking target and adjusting its posture and distance from the target according t...

AI Technical Summary

Problems solved by technology

However, the current tracking algorithms are limited to providing rectangular frames for subsequent algorithms, with poor scalability and usually only able to access some specific intelligent systems.

Moreover, the current tracking algorithm needs to incorporate a large amount of well-designed and highly task-customized expert experience into the algorithm, and the implementation process is relatively complicated.

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