Behavior recognition method based on multi-instance markov model

A Markov model and recognition method technology, applied in character and pattern recognition, instruments, computer parts, etc., to achieve the effect of reducing annotation

Inactive Publication Date: 2014-01-29
INST OF AUTOMATION CHINESE ACAD OF SCI
View PDF5 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to solve the problem of behavior recognition in complex scen

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 multi-instance markov model
  • Behavior recognition method based on multi-instance markov model
  • Behavior recognition method based on multi-instance markov model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0019] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0020] figure 1 It is a flow chart of the behavior recognition method based on the multi-instance Markov model proposed by the present invention, such as figure 1 As shown, the described behavior recognition method based on multi-instance Markov model comprises the following steps:

[0021] Step S1, establish a training set, the training set contains positive and negative sample videos of a certain action A; extract its local spatiotemporal interest points for each video in the training set; perform the first aggregation on the extracted spatiotemporal interest points Class, get these spatio-temporal interest points corresponding to the category of the first cluster center;

[0022] For example, clustering the extracted...

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 behavior recognition method based on a multi-instance markov model. The behavior recognition method includes the following steps that local features of each video are abstracted and a feature histogram of a local video block is used for expressing certain local movement of behavior; a random sampling manner is used for obtaining multiple local video blocks, the local video blocks form multiple markov chains and the markov chains are expressed as continuous movement of some local movement in time; under a frame of multi-instance study, the model selects the markov chain with the most recognition performance to express behavior. In the time of testing, multiple markov chains are composed in the same manner to express the videos and the scores of the markov chains are calculated. If the scores of the markov chains are larger than a certain threshold value, the markov chains belong to the behavior, and otherwise, the markov chains do not belong to the behavior. According to the multi-instance markov model, the aim of behavior recognition under complicated scenes is achieved and labels of the videos are reduced.

Description

technical field [0001] The invention belongs to the technical field of intelligent video monitoring, and in particular relates to a behavior recognition method based on a multi-instance Markov model. Background technique [0002] Behavior recognition has a wide range of applications in video intelligent surveillance. For example, in some specific occasions, airports, squares, streets, shops, etc., the early warning of dangerous behaviors of individuals and groups has important application value for public safety. At present, the mainstream behavior recognition is mainly through the construction of word bags based on local features. This method does not consider the distribution information and semantic information of behavioral feature points in time and space, and these local feature detectors detect many noisy feature points from the background. In order to solve these problems, Sadanand et al. proposed a new behavior representation method, which incorporates semantic in...

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): G06K9/62
Inventor 王春恒周文肖柏华张重
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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