Human body target tracking method based on Generative Adversarial Net (GAN) negative sample enhancement

Abstract

The invention discloses a human body target tracking method based on Generative Adversarial Net (GAN) negative sample enhancement, a DRAGAN network model and an MDNet algorithm model which are built are pre-trained, then a target rectangular box of a human body target calibrated by the first frame is adopted to initialize the MDNet algorithm, a sample queue of the MDNet algorithm model and a training set of the DRAGAN network model are initialized, and the MDNet algorithm model is continuously adopted to track; and when the DRAGAN network model completes update training for the first time, a generator network thereof is adopted to generate a batch of negative samples as part of negative samples adopted in update training of the MDNet algorithm model, update training is performed on the MDNet algorithm model according to the need, and positive samples obtained according to a tracking result is periodically adopted to perform update training on the DRAGAN network model. The human body target tracking method based on GAN negative sample enhancement can improve the degree of accuracy of human body target tracking of the MDNet algorithm model, suppress tracking drifting, and enhance algorithm robustness.

Description

technical field [0001] The invention belongs to the technical field of target tracking, and more specifically relates to a human target tracking method based on generative adversarial network negative sample enhancement. Background technique [0002] As one of the most challenging key technologies in the field of computer vision, target tracking technology has a wide range of applications in many fields such as intelligent monitoring, human-computer interaction, unmanned driving, virtual reality and even military affairs. With the rapid development of social economy, although all walks of life have gradually realized informationization and mechanization, they still need a lot of manpower. At the same time, various public and leisure and entertainment places are also increasing, and people's activities in public places are becoming more and more frequent. How to track human objects in public places and important industrial production environments has always been a hot applic...

AI Technical Summary

Problems solved by technology

The basic idea and principle of CGAN with added conditional constraints has also been successfully applied to the fields of text image conversion, image filling, data prediction and even video and 3D data, but it has not been applied to the field of human target tracking.

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