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Multi-feature and group sparse-based visual object tracking method

A target tracking, multi-feature technology, applied in the field of visual target tracking, can solve problems such as low accuracy and poor stability

Inactive Publication Date: 2016-12-07
ACADEMY OF BROADCASTING SCI STATE ADMINISTATION OF PRESS PUBLICATION RADIO FILM & TELEVISION
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  • Application Information

AI Technical Summary

Problems solved by technology

Although the above methods can track targets to a certain extent, they also have problems such as low accuracy and poor stability.

Method used

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  • Multi-feature and group sparse-based visual object tracking method
  • Multi-feature and group sparse-based visual object tracking method
  • Multi-feature and group sparse-based visual object tracking method

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Embodiment Construction

[0061] Embodiments of the present invention are described in further detail below in conjunction with the accompanying drawings:

[0062] A visual target tracking method based on multi-features and group sparsity, comprising the following steps:

[0063] Step 1. Perform multi-feature extraction on the target in the current frame of the video, and the extracted features include grayscale features, color features, and LBP features.

[0064] The present invention uses multiple features to describe the tracking target, and the used features include grayscale features, color features and LBP features. The grayscale feature is the most commonly used global feature. The amount of information contained in the grayscale image is greatly reduced, and the amount of calculation required is also greatly reduced. Color features describe the surface properties of the scene corresponding to an image or an image region, and play an important role in object tracking in color images. LBP featu...

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Abstract

The invention relates to a multi-feature and group sparse-based visual object tracking method. The technical feature is that the method comprises the following steps of carrying out multi-feature extraction in a target in the current frame of a video; constructing a learning dictionary under different features by utilizing multi-feature information; carrying out particle sampling in a new video frame; removing unqualified particles by adoption of boundary particle re-sampling, and solving a sparse optimization equation for the remaining particles; updating templates, investigating a cosine similarity between a local frame result and a template with the maximum coefficient; if the similarity is less than a certain value, replacing a template with the minimum coefficient by the current template; and if the video is unfinished, carrying out re-sampling. According to the method, multi-feature, particle filtration and group sparse learning technologies are fused; through tracking multiple features of objects, the constructed dictionary contains richer target information; the tracking precision of the overall algorithm is increased; the stability of the tracking result is improved; and a good visual object tracking result is obtained.

Description

technical field [0001] The invention belongs to the field of visual target tracking, in particular to a visual target tracking method based on multi-features and group sparseness. Background technique [0002] Video-based moving target tracking technology is one of the core research topics in the field of computer vision. The main purpose is to imitate the motion perception function of the physiological visual system. The two-dimensional coordinate position in ; Then, according to the feature value related to the moving object, associate the same moving object between consecutive frames in the image sequence, and obtain the motion parameters of the object in each frame image and the correspondence of the moving object between adjacent frame images relationship, so as to obtain the complete trajectory of each moving object, that is, to establish the corresponding relationship of moving objects in the continuous video sequence. [0003] The core of the visual target tracking ...

Claims

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Application Information

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IPC IPC(8): G06T7/20
CPCG06T7/20G06T2207/10016G06T2207/10024G06T2207/20081
Inventor 莫博瑞周芸付光涛
Owner ACADEMY OF BROADCASTING SCI STATE ADMINISTATION OF PRESS PUBLICATION RADIO FILM & TELEVISION
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