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Video target tracking method based on compressed regularization block difference

A target tracking and video technology, applied in image analysis, image data processing, instruments, etc., can solve the problems of unreliability of NPD feature vector, interference of single pixel value noise, affecting the speed of video target tracking, etc.

Active Publication Date: 2018-05-01
YUNNAN UNIV +1
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  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

[0007] (2) NPD features are susceptible to noise interference and the dimension is too high
However, due to the limitation of imaging equipment or digitization process, single pixel value is easily disturbed by noise, if it is directly introduced into the video target tracking process, it will lead to the unreliability of NPD feature vector
Furthermore, the high dimensionality of NPD features will affect the speed of video target tracking

Method used

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  • Video target tracking method based on compressed regularization block difference
  • Video target tracking method based on compressed regularization block difference
  • Video target tracking method based on compressed regularization block difference

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Embodiment Construction

[0119] The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments:

[0120] Such as figure 1 As shown, according to the technical solution of the present invention, the target in a video frame sequence Basketball is tracked as follows, and the scene features are perspective changes, similar interference, occlusion, etc.

[0121] Step 1. Select the tracking area

[0122] The video frame width and height are W=576 and H=432 respectively. The rectangular area (198, 214, 34, 81) of the target to be tracked in the first frame, that is, the coordinates of the upper left corner are (198, 214), and the width and height are (34, 81).

[0123] Step 2. Initialize the measurement matrix

[0124] Compression Measurement Matrix The number of rows, that is, the dimension of the CNBD feature vector is m=100, the number of columns, that is, the dimension of the NBD feature vector is n=1.6...

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Abstract

The invention discloses a video target tracking method based on compressed regularization block difference. The method is realized by the steps that 1) a tracking area is selected; 2) a measurement matrix is initialized; 3) a target classifier is initialized; 4) a target classifier is updated; 5) a new video frame is input; 6) a rough candidate target set is generated; 7) CNBD characteristic vectors of all candidate targets in the rough candidate target set are calculated; 8) a rough tracking result is discriminated; 9) a fine candidate target set is generated; 10) CNBD characteristic vectorsof all candidate targets in the fine candidate target set are calculated; 11) a present-frame tracking result is discriminated; and 12) if the present frame is the last frame, tracking is completed, and otherwise, the step 4) is turned to. According to the method, differential characteristics of a compressed regularization block is used to describe the tracked or candidate targets, and the candidate target set is generated in a rough to fine sliding window manner.

Description

technical field [0001] The invention relates to the field of video target tracking methods, in particular to a video target tracking method based on compressed regularized block difference. Background technique [0002] Video object tracking is to use discriminative features to track moving objects in video frame sequences to analyze their motion parameters and trajectories. However, factors such as object deformation, illumination changes, occlusion, and background chaos in actual scenes have brought great challenges to video object tracking technology. Many applications such as intelligent video surveillance, robot navigation, and human-computer interaction require video object tracking methods to be both accurate and fast. [0003] In the whole process of the video target tracking method, prior art 1Wang N, Shi J, Yeung D Y, et al. Understanding and diagnosing visual tracking systems[C] / / Proceedings of the IEEE International Conference on Computer Vision.2015:3101-3109. ...

Claims

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Application Information

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IPC IPC(8): G06T7/246G06T7/223
CPCG06T2207/10016G06T7/223G06T7/246
Inventor 高赟张登卓周浩张晋林宇兰戈
Owner YUNNAN UNIV
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