Deep learning-based intelligent indoor intrusion detection method and system

A technology of deep learning and intrusion detection, which is applied in closed-circuit television systems, instruments, biological neural network models, etc., can solve the problems of inaccurate detection and identification of moving objects and intruders, save server overhead, improve training speed, and ensure convergence correctness effect

Inactive Publication Date: 2017-02-01
NANJING UNIV OF POSTS & TELECOMM
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  • Abstract
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  • Claims
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AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art, provide an intelligent indoor intrusion detec...

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  • Deep learning-based intelligent indoor intrusion detection method and system
  • Deep learning-based intelligent indoor intrusion detection method and system
  • Deep learning-based intelligent indoor intrusion detection method and system

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Embodiment Construction

[0040] Embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0041] like figure 1 As shown, the present invention has designed a kind of intelligent indoor intrusion detection method based on deep learning, comprising the following steps:

[0042] Step 1. Establish a BP neural network model, and train the BP neural network model according to input training data including user images. In the method, the first time the model is used to input training data, the BP neural network model is trained, and the steps include initializing weights and thresholds, adopting the PRP conjugate gradient algorithm to adjust the weights and thresholds layer by layer, iterating to the maximum iteration frequency. The process is as follows:

[0043] Input training data to the BP neural network model using the PRP conjugate gradient algorithm, the training data at least includes user body images and user face images with different postures,...

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Abstract

The invention discloses a deep learning-based intelligent indoor intrusion detection method and system. The method comprises the following steps of establishing a BP neural network model; obtaining difference images between adjacent frames in a monitoring video picture by utilizing a frame difference algorithm; carrying out binarization processing on the obtained difference images and extracting change foreground area images, under a static background, in the processed images; detecting and identifying whether human shapes exist in the extracted change foreground images or not; when the human shapes exist, detecting and extracting face area images from the change foreground areas; identifying whether the extracted face area images are user images or not; and when the face area images are not the user images, determining the face area images as non-user intrusion and sending alarm signals to the users. The method and system are strong in ability of resisting the interference of other moving objects and low in mis-judgement rate, and can be used for carrying out massive video data analysis and correctly carrying out intrusion detection and identification.

Description

technical field [0001] The invention relates to an intelligent indoor intrusion detection method and system based on deep learning, belonging to the technical field of video surveillance. Background technique [0002] With the development of economy and science and technology, people pay more and more attention to indoor security. Intelligent indoor intrusion detection system has become a trend, and then research on the intelligence, accuracy and real-time performance of intrusion target detection and identification become very meaningful. [0003] In the traditional intelligent monitoring method, a camera and an infrared detector are used to sense whether there is a moving object intruding into its sensing range, and if an intruder is detected, the video information is sent to the mobile terminal. [0004] However, there are defects in the traditional monitoring method. Specifically, the method cannot determine whether the intruder is a user, has low anti-interference abil...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06N3/02H04N7/18
CPCH04N7/18G06N3/02G06V40/161G06V40/168
Inventor 胡婧覃婷婷成孝刚邵文泽成云李德志李海波
Owner NANJING UNIV OF POSTS & TELECOMM
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