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Fixed-lens real-time monitoring video feature extraction method based on SIFT feature clustering

A technology of real-time monitoring and video features, applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve problems such as feature extraction that are not suitable for monitoring video

Active Publication Date: 2016-12-07
SOUTH CHINA UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the surveillance video is special. Most of the surveillance videos are in the same shot for a long time, and the shot switching is not obvious in the surveillance video. Therefore, the extraction method based on shot segmentation is not suitable for the feature extraction of the surveillance video.

Method used

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  • Fixed-lens real-time monitoring video feature extraction method based on SIFT feature clustering
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  • Fixed-lens real-time monitoring video feature extraction method based on SIFT feature clustering

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Embodiment 1

[0064] The embodiment of the present invention provides a real-time video feature extraction method for the surveillance video which is in the same shot for a long time and basically does not have the feature of shot switching, hereinafter referred to as the method.

[0065] In this method, SIFT feature technology is needed, which is a basic technology used in this method. Its role in this method is to extract feature points from each video frame.

[0066] The following is a brief introduction to SIFT.

[0067] SIFT, or Scale-invariant feature transform (SIFT) for short, is a local image feature extraction algorithm proposed by Professor David G. Lowe in 1999 and further improved in 2004. The SIFT feature is a local feature of an image. Its feature points have good stability and are not affected by image rotation, scaling, and affine. It has a high anti-interference ability for external interference factors such as light and viewing angle changes.

[0068] At the same time, ...

Embodiment 2

[0133] In this embodiment, the description of the specific implementation and effect of this method is carried out with the processing of a video segment SL05_540P.

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Abstract

The invention discloses a fixed-lens real-time monitoring video feature extraction method based on SIFT feature clustering and the method comprises the steps: carrying out the feature extraction of each frame of a monitoring video, generated in real time, in a mode of parallel computing through employing an SIFT feature extraction algorithm; enabling the monitoring video stream generated in real time to be segmented into video segments according to the rule that each video segment comprises the same content; and respectively extracting a special key frame of the each video segment after segmenting. The method effectively separates the video segments with the similar contents from the monitoring video, effectively extracts the key frame from the similar video segments through employing a key frame extraction method based on a maximum feature point strategy, reduces the redundancy of the key frame, achieves the better video feature extraction effect, and provides a basis for the content retrieval of a large number of monitoring videos. Meanwhile, the method effectively solves a difficulty that the time cost of the feature extraction of video frames is large through enabling the processes of feature extraction of the video frames to be parallel, and improves the instantaneity.

Description

technical field [0001] The invention relates to the technical field of multimedia information processing, in particular to a fixed lens real-time monitoring video feature extraction method based on SIFT feature clustering. Background technique [0002] Video features are an effective description of video content. Extracting video features to index massive video databases is an effective method to solve the problem of content-based retrieval in massive videos. [0003] The current video feature extraction methods mainly include three key technologies: image bottom-level feature extraction, video segmentation and key frame extraction. Video feature extraction. However, the surveillance video is special. Most of the surveillance videos are in the same shot for a long time, and the shot switching is not obvious in the surveillance video. Therefore, the extraction method based on the shot segmentation is not suitable for the feature extraction of the surveillance video. Therefo...

Claims

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Application Information

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IPC IPC(8): G06K9/00
CPCG06V20/41G06V20/46
Inventor 徐杨梁肇浩高勒
Owner SOUTH CHINA UNIV OF TECH
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