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Moving object segmenting method based on local space-time manifold learning

A technology of moving objects and space-time flow, applied in image analysis, image data processing, instruments, etc., can solve problems such as sudden light changes, complex dynamic backgrounds, etc.

Active Publication Date: 2012-10-24
SUN YAT SEN UNIV
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AI Technical Summary

Problems solved by technology

[0014] In order to solve the problem of complex dynamic background and sudden illumination changes in moving target segmentation, the present invention provides stable and reliable foreground areas for moving target tracking, target behavior recognition, and cross-line / cross-region detection in intelligent video surveillance.

Method used

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  • Moving object segmenting method based on local space-time manifold learning
  • Moving object segmenting method based on local space-time manifold learning
  • Moving object segmenting method based on local space-time manifold learning

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Embodiment Construction

[0042] specific implementation plan

[0043] Such as figure 1 Said, the present invention comprises the following steps:

[0044] 1) Input offline video;

[0045] 2) Perform spatio-temporal cube segmentation and symmetric spatio-temporal local texture coding feature extraction on the offline video, and obtain the spatio-temporal texture change description sequence of spatio-temporal cube features;

[0046] 3) Establish a background model based on local spatio-temporal manifold learning according to the spatio-temporal texture change description sequence of spatio-temporal cube features;

[0047] 4) Input motion video;

[0048] 5) Perform spatio-temporal cube segmentation and symmetric spatio-temporal local texture coding feature extraction on motion video;

[0049] 6) By judging the distance between the space-time cube of the motion video and the space-time cube predicted by the background model, the target motion and background part are obtained;

[0050] 7) Update and mai...

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Abstract

The invention discloses a moving object segmenting method based on local space-time manifold learning. The method comprises the following steps: 1. segmentation of a space-time cube of an input video and extraction of an illuminant invariance feature; 2. establishment of a background model based on the local space-time manifold learning; and 3. segmentation of a moving object based on the local space-time manifold and on-line updating maintenance of a model. According to the moving object segmenting method based on the local space-time manifold learning, local space-time variation can be effectively described, the scale adaptability of an SIFT (scale invariant feature transform) feature point set in an extended image in the input video is treated, the problem that a dynamic background and illumination change can not be effectively eliminated when the moving object is segmented is solved, and the reliable and effective moving object can be provided for an intelligent monitoring platform.

Description

technical field [0001] The present invention relates to the field of video surveillance, and specifically relates to moving target segmentation technology, extraction of illumination invariance features, local space-time manifold offline learning, moving target and background segmentation, local space-time manifold online update and maintenance and other fields. technical background [0002] At present, the public place security system with video surveillance as the core is being greatly promoted. Different from the huge manpower cost and the possible problems of a large number of false positives and false positives brought about by traditional human video surveillance, intelligent video surveillance is characterized by its Intuitive, accurate, timely and rich in information, it has attracted more and more attention and promotion from the industry. In recent years, with the rapid development of computer performance and network, the computing bottleneck and transmission bottl...

Claims

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Application Information

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IPC IPC(8): G06T7/20
Inventor 林倞江波徐元璐梁小丹
Owner SUN YAT SEN UNIV
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