Online target tracking method based on increment bilateral two-dimensional principal component analysis (Bi-2DPCA) learning and sparse representation

A sparse representation and target tracking technology, which is applied in the field of online target tracking based on incremental Bi-2DPCA learning and sparse representation, can solve the problems of low real-time performance and cannot describe the target feature space structure and changes well, and achieves Good noise and occlusion problems, fast and accurate tracking algorithms, and easy handling

Active Publication Date: 2013-12-25
NANTONG JUJIU NEW MATERIAL SCI & TECH CO LTD
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Problems solved by technology

This method can handle noise and occlusion problems well, but the real-time performance is not high, and this method directly uses the target template as the basis of the sparse dictionary, which cannot describe the structure and changes of the target feature space well.

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  • Online target tracking method based on increment bilateral two-dimensional principal component analysis (Bi-2DPCA) learning and sparse representation
  • Online target tracking method based on increment bilateral two-dimensional principal component analysis (Bi-2DPCA) learning and sparse representation
  • Online target tracking method based on increment bilateral two-dimensional principal component analysis (Bi-2DPCA) learning and sparse representation

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Embodiment Construction

[0025] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0026] 1) Mark the target x in the first frame 1 (x 1 is the affine transformation parameter of the target image block in the first frame), initialize N particles and their weights

[0027] 2) The first T frames of images use the classic particle filter algorithm to track the target, and get the initial target sample set A={A 1 ,A 2 ,...,A T}, A i Represents the target image block matrix in the i-th frame image, whose size is normalized to m×n;

[0028] 3) Calculate the covariance matrix of A in is the mean value of the elements in A. to G T Perform eigenvalue decomposition (EVD), and take the first q larger eigenvalues The corresponding eigenvectors form the right transformation matrix Put the elements in A in R T Up projection, get: P=AR T . Calculate P T The covariance matrix of Perform eigenvalue decomposition (EVD) on F, and take the f...

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Abstract

The invention relates to an online target tracking method based on increment bilateral two-dimensional principal component analysis (Bi-2DPCA) learning and sparse representation, and provides an increment Bi-2DPCA learning algorithm capable of rapidly and accurately updating a target sub-space model to reflect an appearance change of a target in a tracking process. Aiming at the problem that the target is frequently blocked and polluted by noise in the tracking process to cause that the tracking effect becomes worse, according to the method disclosed by the invention, a Bi-2DPCA-based sub-space model is embedded below a sparse representation frame, so that interferences to target positioning and target sub-space model updating caused by blocking and noise are furthest removed. Meanwhile, a novel method for calculating visual similarity is used. By adopting the method, energy distribution of Bi-2DPCA when an image is presented is considered, and is more accurate in comparison with a classical reconstruction error; tracking is achieved under a Bayesian inference framework; the target state is estimated by using a particle filtering algorithm.

Description

technical field [0001] The invention relates to an online target tracking method based on incremental Bi-2DPCA learning and sparse representation, which is a subspace representation model combined with Bilateral two-dimensional Principal Component Analysis (Bi-2DPCA) and sparse representations for online object tracking. Background technique [0002] Object tracking is a fundamental problem in the field of computer vision. It has a wide range of applications: including video surveillance, behavior analysis, motion event detection, and video retrieval. Although many scholars have made a lot of efforts in this field, visual tracking is still a challenging research field. Because the appearance of the target often faces changes caused by illumination changes, occlusion, deformation, complex moving background, etc. during the tracking process. Therefore, a good target appearance model will play a decisive role in the robustness of the tracking algorithm. [0003] As a classi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/20
Inventor 李映宋旭李鹏程
Owner NANTONG JUJIU NEW MATERIAL SCI & TECH CO LTD
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