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
View PDF4 Cites 19 Cited by
  • 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

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Fixed-lens real-time monitoring video feature extraction method based on SIFT feature clustering
  • Fixed-lens real-time monitoring video feature extraction method based on SIFT feature clustering
  • Fixed-lens real-time monitoring video feature extraction method based on SIFT feature clustering

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0063] Example one

[0064] The embodiment of the present invention provides a real-time video feature extraction method for the surveillance video that is in the same lens for a long time and basically has no feature of lens switching, which is hereinafter referred to as this method.

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

[0066] Below 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 was 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 high anti-interference ability against external interference factors such as light and viewing angle changes.

[0068] At the s...

Example Embodiment

[0132] Example two

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

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/00
CPCG06V20/41G06V20/46
Inventor 徐杨梁肇浩高勒
Owner SOUTH CHINA UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap