Moving target detection method based on optical flow field clustering

A moving target and detection method technology, applied in image data processing, instruments, calculations, etc., can solve the problems of low precision, time-consuming calculation, limited optical flow field modeling, and insufficient use of effective information of optical flow to achieve detection high rate effect

Active Publication Date: 2017-01-18
ZHEJIANG ICARE VISION TECH
View PDF7 Cites 10 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Most of the existing methods use the optical flow field as a supplement to the background modeling or target detector, such as the patent [200910236053.2], which does not make full use of the effective informatio

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
  • Moving target detection method based on optical flow field clustering

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0018] Below in conjunction with embodiment and attached figure 1 The present invention is described further:

[0019] For a 100-frame video, the DeepFlow algorithm is used to obtain the optical flow field of each frame, and the optical flow field of each frame is composed of two images in the x direction and y direction.

[0020] For the optical flow field of each frame, the DENCLU algorithm is used to perform clustering on the two-dimensional optical flow vector to obtain cluster blobs, and the average value of the optical flow of the pixels contained in each blob is recorded as the optical flow vector of the blob.

[0021] For each blob, traverse the blobs adjacent to it, and if it is found that the Euclidean distance of the optical flow vector is less than 1, it will be merged, and the optical flow vector of the blob after merging is calculated according to the weighted average of the area of ​​the blob before merging.

[0022] Each blob is iterated over until no more mer...

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 moving target detection method based on optical flow field clustering. The method comprises the following steps: to begin with, generating a dense optical flow field for each frame of image of a video through a DeepFlow algorithm; then, carrying out optical flow field clustering by utilizing a DENCLUE algorithm, and clustering motion vector similar pixels into blob; then, merging the motion vector similar blob; and finally, in the time aspect, merging the adjacent multiframe blob and forming a moving target tracking path. The method has good robustness for interferences of illumination variation, shadow, noise and random swing and the like, can naturally segment the targets, the motion vectors of which are inconsistent, has a great application value for follow-up classification, tracking and retrieval and the like, is high in detection rate, and can better detect the moving targets, which can be recognized by human eyes, in the video.

Description

technical field [0001] The invention belongs to the technical field of intelligent video monitoring, and relates to a moving target detection method based on optical flow field clustering. Background technique [0002] At present, video-based moving target detection methods can be roughly divided into the following categories: [0003] A method based on background modeling. For example, the patent [200910077433.6], the disadvantage of this method is mainly that it cannot overcome the difficulties such as partial illumination and deep shadow misdetection, and it is prone to adhesion for objects with a short distance, which will cause great damage to subsequent tracking and classification. interference. [0004] Machine Learning Based Object Detector Approach. For example, the patent [201510323680.5], the disadvantage of this method is that the effect of the detector is completely dependent on the training samples, and because the target of the actual scene is ever-changing...

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): G06T7/20
CPCG06T7/20G06T2207/10016
Inventor 尚凌辉王弘玥
Owner ZHEJIANG ICARE VISION 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