Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Crowd abnormal event detection method based on three-dimensional pyramid image generation network

A pyramid image and abnormal event technology, applied in the field of graphic recognition, can solve the problems of unfavorable distinction between normal images and abnormal images, uncertainty of abnormal event types, difficulty in expressing abnormal events, and susceptibility to the influence of light

Active Publication Date: 2019-08-06
HEBEI UNIV OF TECH
View PDF11 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In 2016, M Ravanbakhsh and M Nabi used the convolutional neural network (Convolutional Neural Network, referred to as CNN) analyzes the behavior of crowded crowds, and proposes to combine high-level semantic features extracted by CNN and optical flow feature modeling. However, due to the uncertainty of the types of abnormal events and the difficulty in expressing abnormal events, CNN with supervised learning obviously cannot play its own role. advantages and characteristics, and the introduction of the optical flow method to extract the movement features of the crowd still has problems such as being easily affected by light.
In 2017, SC Yong and HT Yong used Auto-Encoder (Auto-Encoder) and Long Short-Term Memory (Long Short-Term Memory, LSTM) to combine space and time in the paper "Abnormal Event Detection In Videos Using Spationtemporal Auto-Encoder" published by Springer information to detect abnormal crowd events, but the image restoration degree generated by the self-encoding network of unsupervised learning is low, and the key information in the image is easy to be lost, which affects the accuracy of the model
CN108280408A discloses a method for detecting crowd abnormal events based on hybrid tracking and generalized linear model, which has the defect of poor versatility in identifying pedestrian abnormal behavior by tracking pedestrian movement trajectories and extracting feature points of the tracking path
CN107729799A discloses a visual detection and analysis early warning system for crowd abnormal behavior based on deep convolutional neural network. In its algorithm, local features at the pixel level extracted by optical flow method are used. Pixel errors will directly affect the feature descriptor. Moreover, the optical flow method has a large amount of calculation, is easily affected by illumination, and only extracts the feature vector for each video frame, ignoring the defects of the correlation relationship between video frames and frames to a certain extent.
CN103258193B discloses a group abnormal behavior recognition method based on KOD energy features, which has the disadvantage of adaptively selecting the threshold value of the added group kinetic energy and directional potential energy, resulting in low adaptability of the algorithm in different scenarios
CN106778595A discloses a method for detecting abnormal behavior in a crowd based on a Gaussian mixture model. In this method, when a long-term static object in the background suddenly moves, false detection and missed detection are likely to occur in the foreground detection. The shadow suppression effect of moving objects is not good, the model modeling has a large amount of computation, and the algorithm has many steps and time consumption, and cannot guarantee the real-time performance of alarm information in video surveillance.
[0006] Although the existing technology has proposed various improved methods for crowd abnormal event detection by generating generated images that are closer to the original images, the quality of the generated images is still not high, the spatiotemporal information and local information of different scales in the image sequence are lost, and the normal images and The reconstruction error of abnormal images is small, the training time is long, the network training is unstable, and it is not conducive to distinguishing between normal images and abnormal images.

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
  • Crowd abnormal event detection method based on three-dimensional pyramid image generation network
  • Crowd abnormal event detection method based on three-dimensional pyramid image generation network
  • Crowd abnormal event detection method based on three-dimensional pyramid image generation network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0104] The method for detecting abnormal crowd events based on the three-dimensional pyramid image generation network of the present embodiment, the specific steps are as follows:

[0105] In the first step, the video of crowd activities is converted into a sequence of images:

[0106] Get a group of video sequences of crowd activities, use OpenCV to extract N frames of images f from any crowd video i i1 , f i2 ,...,f iN The sequence of images that make up video i, denoted as F i {f i1 , f i2 ,...,f id ,...,f iN}, where f id Represents the dth frame image of the image sequence extracted from video i, N is 200, for the obtained image sequence F i The images in the middle are standardized, and the size of the images is normalized to M×M pixels, and M is 256; the set of image sequences extracted from all video sequences is T{F 1 ,F 2 ,...,F i ,...,F q}, where q represents the number of video sequences, F i Represents the image sequence of the i-th video, denoted as F...

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 crowd abnormal event detection method based on a three-dimensional pyramid image generation network, and relates to a method for identifying graphs. The method includes: generating an image by using a three-dimensional pyramid image generation network; and detecting the crowd abnormal event by comparing the difference between the generated image generated by the three-dimensional pyramid image generation network and the to-be-detected image. The defects that in the prior art, motion information between image sequences, local characteristics of different scales and reconstruction errors between normal images and abnormal images are ignored and crowd abnormal behaviors are difficult to judge in an abnormal detection method based on generated images are overcome.

Description

technical field [0001] The technical solution of the present invention relates to a method for recognizing graphics, in particular to a method for detecting abnormal crowd events based on a three-dimensional pyramid image generation network. Background technique [0002] With the increasingly prominent public security issues and the popularization of video surveillance equipment, crowd anomaly event detection based on video surveillance can detect abnormalities in the crowd in time and avoid unnecessary losses, so it has important research significance in the field of public security. [0003] There are two main ways to detect abnormal crowd events: the traditional way and the way based on deep learning. The traditional method mainly extracts features from the aspects of optical flow and gradient, and then uses SVM for classification. In the traditional way of crowd abnormal event detection, due to the characteristics of its own algorithm, only some simple and basic feature...

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): G06K9/00
CPCG06V20/42G06V20/53G06V20/46
Inventor 郭迎春师硕郝小可朱叶刘依于洋阎刚王柏林
Owner HEBEI UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products