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A video abnormal behavior detection method based on depth learning

A technology of deep learning and detection methods, which is applied in the field of video processing and can solve problems such as crowd occlusion.

Active Publication Date: 2019-02-19
HANGZHOU DIANZI UNIV
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Problems solved by technology

In the early days, some scholars detected abnormal events by studying the trajectory of moving objects in the video, but this method could not solve the problem of crowd occlusion

Method used

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  • A video abnormal behavior detection method based on depth learning
  • A video abnormal behavior detection method based on depth learning
  • A video abnormal behavior detection method based on depth learning

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Embodiment Construction

[0026] The present invention will be described in detail below in conjunction with the accompanying drawings and implementation examples.

[0027] A method for detecting abnormal video behavior based on deep learning, including a training phase and a testing phase.

[0028] The training phase consists of three modules: 1. Preprocessing module. The main function of this module is to obtain the grayscale data and optical flow data of the training data set; 2. Build a residual self-encoding network module. The main function of this module is to build a fusion The residual autoencoder network structure of a variety of neural network structures is introduced; 3. Training neural network, the main function of this module is to use the training data that only contains normal behavior to train the residual autoencoder network, and obtain the model based on grayscale image reconstruction and light Two models for flow graph reconstruction.

[0029] The test phase is also composed of thr...

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Abstract

The invention relates to a video abnormal behavior detection method based on depth learning. The method includes a training phase and a test phase. In the training phase, the training video sequence is converted into gray-scale image and optical flow image, which are respectively made into space-time blocks, and then input into residual self-coding model for training. Two models based on gray-scale image and optical flow image are established. The trained model contains the motion mode and appearance information of normal behavior, so it is more conducive to the reconstruction of normal behavior. In the test phase, the test data is made into space-time blocks of gray-scale map and optical flow map, At first, that space-time block of the grayscale image is input into the grayscale image model, The invention initially detects a normal area and a suspicious area, inputs the optical flow map space-time block of the suspicious area into the optical flow map model, detects the normal area and the abnormal area, and obtains the final abnormal judgment. The method of the invention can better identify the motion information and the appearance characteristic of the video data, thereby improving the detection rate of the abnormal behavior.

Description

technical field [0001] The invention belongs to the technical field of video processing, and relates to a method for detecting abnormal behaviors of videos, in particular to a method for detecting abnormal behaviors of videos based on deep learning. Background technique [0002] Video abnormal behavior detection belongs to the category of intelligent video surveillance. It uses intelligent algorithms to detect abnormal behaviors in surveillance videos and sends out alarm signals to improve the response speed of relevant departments. The development of abnormal video behavior detection technology plays an important role in maintaining the safety of public places and saving manpower and material resources. [0003] The definition of abnormality is different in different video scenes, and the types of abnormal events in the same scene are also relatively diverse. Usually, an abnormal event is an event that is different from a normal event and has a relatively small probability...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/46G06F18/214
Inventor 陈华华刘萍郭春生叶学义
Owner HANGZHOU DIANZI UNIV
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