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Background modeling method (method of segmenting video moving object) based on space-time video block and online sub-space learning

A technology of background modeling and moving objects, which is applied in the field of video, video content analysis and target detection, and can solve the problems that the lighting changes cannot work well.

Inactive Publication Date: 2010-02-10
湖北莲花山计算机视觉和信息科学研究院
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Problems solved by technology

It does not work well for drastic lighting changes in the scene, etc.

Method used

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  • Background modeling method (method of segmenting video moving object) based on space-time video block and online sub-space learning
  • Background modeling method (method of segmenting video moving object) based on space-time video block and online sub-space learning
  • Background modeling method (method of segmenting video moving object) based on space-time video block and online sub-space learning

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Embodiment Construction

[0022] Concrete implementation method of the present invention is as follows:

[0023] 1. Model initialization:

[0024] For a video block sequence B={x 1 , x 2 ,...,x n , ...}, we first perform a traditional principal component analysis (batch PCA) algorithm on some previous video blocks (eg, the first 200) to obtain a low-dimensional subspace (eg, d = 8 dimensions). Then use it as the initial space for online subspace learning and updating for normal background maintenance and foreground detection. This initialization is optional. If the computing resources are not allowed, such as the embedded memory is small, you can directly perform the update detection of the model without this initialization process. It's just that if there is no initialization, the background modeling effect for a short period of time will not be good, and it will take a while to learn.

[0025] 2. Background model matching and moving target detection:

[0026] For a new incoming video block x n ,...

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Abstract

The invention relates to the video field, in particular to the content analysis of the video and the object detection field. The purpose of the invention is to solve problem that the moving object segmentation is easily affected by illumination changes in the application of video monitoring, such as abrupt change of solar illumination in daytime, automobile light at night, a great amount of falsealarm can be generated by using the traditional method. Two key technologies are utilized in the invention for realizing the above purpose. One key technology is to take a space-time video block as abasic process unit, thus apparent spatial information and time motion information are simultaneously utilized to carry out background modeling and prospective detection segmentation. The other key technology is to effectively capture background modeling by utilizing online sub-space learning method. The method can be used in the systems for processing and analyzing the video content, which need tocarry out background modeling and prospective detection, such as video monitoring system.

Description

technical field [0001] The invention relates to the field of video, in particular to the field of video content analysis and target detection. The purpose of the present invention is to solve the problem that target segmentation in video surveillance applications is easily affected by illumination changes, such as sudden changes in sunlight during the day, car lights at night, etc. Traditional methods will generate a large number of false alarms. And the present invention can well solve this problem. Background technique [0002] The background modeling of video with a fixed camera angle of view refers to a technology that uses mathematical models and algorithms to establish a mathematical model of the static background in a continuous image sequence. Using this background model, the image area of ​​the moving target in the video sequence can be automatically segmented from the background. This technology can be used in various application fields such as video intelligent ...

Claims

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Application Information

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IPC IPC(8): G06T7/20
Inventor 朱松纯赵友东
Owner 湖北莲花山计算机视觉和信息科学研究院
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