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On-line target tracking method based on probabilistic principal component analysis and compressed sensing

A principal component analysis, compressed sensing technology, applied in image analysis, image data processing, instruments, etc., to achieve good real-time, guaranteed accuracy, accurate calculation results

Inactive Publication Date: 2013-09-18
江苏星地通通信科技有限公司
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Problems solved by technology

However, this method has to face the huge feature dimension when updating the target feature subspace. Although the incremental PCA algorithm used by Ross et al. reduces the complexity of the algorithm to a certain extent, the feature dimension is still an unavoidable problem. do not consider the problem

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  • On-line target tracking method based on probabilistic principal component analysis and compressed sensing
  • On-line target tracking method based on probabilistic principal component analysis and compressed sensing
  • On-line target tracking method based on probabilistic principal component analysis and compressed sensing

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Embodiment Construction

[0022] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0023] Step 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

[0024] Step 2: Use the classic particle filter algorithm to track the target in the first T frames, and get the initial target sample set A=[y 1 ,y 2 ,...,y T ],y i is the projection of the high-dimensional image feature vector on the measurement matrix P, and is the compressed low-dimensional feature vector, which has:

[0025] the y i =Pv i (1)

[0026] Among them, v i is the aggregated column vector representation of the output of the target image block in the i-th frame image under various scale rectangular filters (v i That is, a high-dimensional image feature vector), P is an equidistant constraint condition, and the size is L*H (Li Dimensions, H is t...

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Abstract

The invention relates to an on-line target tracking method based on probabilistic principal component analysis and compressed sensing. A compressed sensing theory is combined with a PPCA (probabilistic principal component analysis) theory to obtain a quite simple target characteristic subspace representation model, and the target subspace representation model is updated on line by the aid of an incremental PCA (principal component analysis) algorithm to enable a tracking algorithm to achieve good real-time performance. Meanwhile, the PPCA theory is applied to computation of visual similarity of a candidate target and the target subspace representation model to obtain a DFFS (distance from face space) and a DIFS (distributed inter-frame space) not just reconstruction errors in PCA, so that visual similarity is computed more accurately, and further accuracy of the tracking algorithm is guaranteed.

Description

technical field [0001] The invention relates to an online target tracking method based on probability principal component analysis and compressed sensing, in particular to a target tracking method for online updating target feature subspace based on probability principal component analysis and compressed sensing. 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. Target tracking is a challenging research topic because the target will face problems such as illumination changes, occlusion, deformation, and complex moving backgrounds during the tracking process, which often lead to target loss and drift. [0003] In the target tracking algorithm, the target representation model has a great influence on the performance of the algorithm. The existing target representation models mainly include: (a...

Claims

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Application Information

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IPC IPC(8): G06T7/20
Inventor 李映宋旭冉晨
Owner 江苏星地通通信科技有限公司
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