Self-adaptive feature selection and time consistency robust correlation filtering visual tracking method

A feature selection and correlation filtering technology, applied in the field of computer vision, can solve problems such as difficult to achieve the effect of real-time target tracking, and achieve the effect of alleviating weak discrimination, fast speed and good performance

Active Publication Date: 2019-06-07
XIAMEN UNIV
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AI Technical Summary

Problems solved by technology

Compared with traditional tracking methods, the tracking performance of this method has ...

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  • Self-adaptive feature selection and time consistency robust correlation filtering visual tracking method
  • Self-adaptive feature selection and time consistency robust correlation filtering visual tracking method
  • Self-adaptive feature selection and time consistency robust correlation filtering visual tracking method

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Embodiment Construction

[0050] The method of the present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0051] Embodiments of the present invention include the following steps:

[0052] A. In the tth frame, given the located target, the basic sample is constructed from the target and its surrounding background. The training sample is composed of all the circular translation samples of the basic sample. The corresponding label is determined by the Gaussian function. The regressor is trained as follows:

[0053]

[0054] Among them, * is the convolution operation symbol, x t and f t is the training sample and filter of the t-th frame, y is the label determined by the Gaussian function, and D is the dimension of the feature.

[0055] B. Since all the features extracted from the sample (including discriminative features and interference features) are used to train the regressor in step A, in order to select discriminative features while s...

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Abstract

The invention discloses a self-adaptive feature selection and time consistency robust correlation filtering visual tracking method, and relates to a computer vision technology. The elastic network andthe time consistency constraint are introduced into related filtering learning at the same time. The method has the advantages that the discriminative features can be adaptively selected to inhibit the interference features, model learning and updating can be combined together, the problems of poor discriminative property and time-dependent degradation of a traditional related filter can be effectively solved, and the robustness of the algorithm to shielding, deformation, rotation and background interference is improved. Through the elastic network and the time consistency constraint, the correlation filter adaptively selects discriminative features which are continuous in time and have regional characteristics. The derived correlation filtering learning problem can be solved through an ADMM, and the problem can be efficiently solved only through a few iterations. And better performance can be obtained, the precision is high, and the speed is high.

Description

technical field [0001] The invention relates to computer vision technology, in particular to an adaptive feature selection and temporal consistency robust correlation filtering visual tracking method. Background technique [0002] Human beings have a high visual perception ability for external videos, and the brain can quickly and accurately locate moving targets in the video. For a computer to mimic the visual perception capabilities of the human brain, it needs to be able to reach human levels of speed and accuracy. Visual tracking is a basic problem in computer vision and the basic content of visual perception. Its speed and accuracy determine the real-time and accuracy of visual perception. Object tracking is one of the important research directions in the field of computer vision, and it plays an important role in intelligent video surveillance, human-computer interaction, robot navigation, virtual reality, medical diagnosis, public safety and other fields. The task f...

Claims

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Application Information

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IPC IPC(8): G06T7/246
Inventor 王菡子梁艳杰严严刘祎
Owner XIAMEN UNIV
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