Behavior recognition method based on three descriptors of hydrodynamics

A technology of fluid mechanics and recognition methods, applied in the direction of character and pattern recognition, biological models, instruments, etc., can solve the problems of small detection and extraction features, no consideration of influence, small moving targets in high-density monitoring scenes, etc., to achieve accurate high rate effect

Active Publication Date: 2018-09-14
SHANGHAI JIAO TONG UNIV
View PDF2 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] 1) The moving target in the high-density monitoring scene is small, the features detected and extracted from a single individual are extremely small, and the description power for crowd behavior is not enough;
[0006] 2) The features analyzed after being abstracted into particles lose the continuity of the description of crowd behavior;
[0007] 3) Focus on local movement behavior, without considering the overall impact, and lack the description of the entire group

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
  • Behavior recognition method based on three descriptors of hydrodynamics
  • Behavior recognition method based on three descriptors of hydrodynamics
  • Behavior recognition method based on three descriptors of hydrodynamics

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0044] The images used in this embodiment are from the crowd behavior database of PETS09. The PETS09 database is a dataset proposed by the IEEE International Symposium on Tracking and Monitoring Performance Evaluation in 2009.

[0045] like figure 1 As shown, the behavior recognition method based on three fluid dynamics descriptors involved in this embodiment includes the following specific implementation steps:

[0046] The first step: Obtain the motion characteristics of the picture through the optical flow method where u x , u y are the horizontal and vertical components of the particle velocity.

[0047] Step 2: Based on the theory of fluid mechanics, extract three kinds of descriptors of the crowd, namely the advection descriptor, the diffusion descriptor and the pressure descriptor;

[0048] According to the prior art, the known basic equation of fluid mechanics Navier-Stokes equation is as follows:

[0049]

[0050] See: Grassi V, Leo R A, Soliani G, et al. Na...

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 provides a behavior recognition method based on three descriptors of hydrodynamics. The behavior recognition method based on three descriptors of hydrodynamics comprises the steps: calculating three descriptors of a picture, based on hydrodynamics, and respectively obtaining an advection descriptor, a diffusion descriptor and a pressure descriptor of the picture; integrating thermodynamic diagrams of different descriptors of the same frame into the input data type of the adopted neural network, inputting into the trained neural network, performing fine tuning on the neural network to obtain a model of different crowd behaviors; and by classifying the crowd behaviors through the obtained model, obtaining the detected crowd behavior type, and realizing behavior recognition. Thebehavior recognition method based on three descriptors of hydrodynamics has high accuracy for behavior recognition, can reduce a frame in which no event occurs as a frame in which an event occurs, and can also detect more frames in which events occur.

Description

technical field [0001] The invention belongs to the technical field of computer video processing, and in particular relates to a behavior recognition method based on three fluid dynamics descriptors. Background technique [0002] Crowd behavior analysis is an important research topic in the field of computer vision. It mainly analyzes crowd movement behavior in real scenes. Crowd behavior analysis methods can be divided into two types: whole-based and object-based. The whole-based analysis method regards the crowd as a whole , can classify the crowd as normal or abnormal, or identify pre-defined crowd behavior; while the latter regards the crowd as a collection of different individuals, considers the interaction between pedestrians in the scene, and detects abnormal and normal activities in the scene. [0003] Whole-based crowd behavior recognition methods are often used in high-density surveillance scenarios (see: Mehran R, Moore B E, Shah M.A Streakline Representation of F...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/00
CPCG06N3/008G06V20/42G06V20/53
Inventor 杨华杨姝颖
Owner SHANGHAI JIAO TONG UNIV
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