Target tracking method based on sparse feature selection

A target tracking and sparse feature technology, applied in the field of computer digital image processing, can solve the problems of similar objects with illumination changes, low contrast between target and background, interference effects, etc.

Inactive Publication Date: 2015-09-23
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0005] In order to avoid the deficiencies of the prior art, the present invention proposes a target tracking method based on sparse feature selection, which solves the problem that the contrast bet

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  • Target tracking method based on sparse feature selection
  • Target tracking method based on sparse feature selection
  • Target tracking method based on sparse feature selection

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Embodiment Construction

[0031] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0032] 1) The distance around the target in the first frame image is R 1 (R 1 Generally, N is randomly generated within a circular range of 2 to 4). p particle points (N p Generally take 8~10), and record its coordinates (x i ,y i ), i=1,2,...,N p . Each particle represents a target sample. At a distance of target radius R 2 (R 2 Generally, outside the circle of 10~15), N can also be randomly generated n particle points (N n Generally take 50~70), and record its coordinate points (x j ,y j ),j=1,2,...,N n . Each particle point represents a target negative sample; the parameters in the first frame image are [x, y, w, h], where: x, y represent the horizontal and vertical coordinates of the target center, and w, h represent the width of the target and high;

[0033] 2) put N p target positive samples and N n target negative samples and a series of ...

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Abstract

The invention relates to a target tracking method based on sparse feature selection. According to the method, firstly, a target, a background and a to-be-selected target point are expressed by use of Haar-like features; then, feature selection is performed on high-dimensional Haar-like features by use of a special nature of sparse expression, and features well distinguishing the target and background are selected as expression of sample points; and finally, selected sample points are used for training a na ve Bayes classifier, and updating classification is performed online, so that the classifier can reflect the relation between the target and background in real time. A projection matrix is constructed by use of the sparse feature selection method, dimensionality reduction is performed on conventional high-dimensional Haar-like features, the calculated quantity is reduced, meanwhile, features helpful to classification are retained, the target and background can be distinguished more effectively, and rapid robustness tracking of the target is realized.

Description

technical field [0001] The invention belongs to computer digital image processing technology, in particular to a target tracking method based on sparse feature selection. Background technique [0002] Target tracking algorithms can be roughly divided into two categories: generative and discriminative. Generative tracking algorithms usually generate an appearance model for the target to be tracked, and find the candidate target with the highest similarity as the tracking result by matching the appearance model. The discriminative tracking algorithm uses a different method, which regards tracking as a binary classification problem, and trains a classifier through positive and negative samples to distinguish the target from the background. [0003] In recent years, the target tracking method based on particle filter has received great attention. Particle filter is a technique for estimating the motion state from noisy data, which approximates the probability by propagating a l...

Claims

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Application Information

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IPC IPC(8): G06T7/20
CPCG06T7/20G06T2207/10016G06T2207/20024
Inventor 李映杭涛李鹏程
Owner NORTHWESTERN POLYTECHNICAL UNIV
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