Robust target tracking method based on phantom adversarial network

Abstract

The invention discloses a robust target tracking method based on a phantom adversarial network, and relates to a computer vision technology. The method comprises the following steps: firstly, proposing a new illusion confrontation network, aiming at learning nonlinear deformation between sample pairs, and applying the learned deformation to a new target to generate a new target deformation sample.In order to effectively train the proposed phantom confrontation network, deformation reconstruction loss is proposed. The invention provides the target tracking method based on the illusion confrontation network based on the offline training, and the method can effectively alleviate the overfitting problem of the deep neural network in the target tracking process due to the online updating. In addition, in order to further improve the deformation migration quality, a selective variable migration method is provided, and the tracking precision is further improved. The target tracking method provided by the invention obtains a competitive result on the current mainstream target tracking data set.

Description

technical field [0001] The invention relates to computer vision technology, in particular to a robust target tracking method based on hallucination confrontation network. Background technique [0002] In recent years, the application of deep neural networks in the field of computer vision has achieved great success. As one of the basic problems in the field of computer vision, target tracking plays a very important role in many current computer vision tasks, such as unmanned driving, augmented reality, robotics and other fields. Recently, the research on target tracking algorithm based on deep neural network has received extensive attention from researchers at home and abroad. However, unlike other computer vision tasks (such as target detection and semantic segmentation), the application of deep neural networks in target tracking tasks is still very effective. The main reason is that the target tracking task itself has certain particularities and lacks diversity. online t...

AI Technical Summary

Problems solved by technology

However, due to the extremely limited online samples (especially target samples), the online learning of such methods is very limited, and it is still easy to cause the problem of overfitting, which affects the tracking performance.

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