A video anomaly detection method based on ST-Unet

An anomaly detection and video technology, applied in the fields of computer vision and pattern recognition, can solve problems such as ignoring spatio-temporal features, and achieve good modeling effect and high accuracy

Active Publication Date: 2019-05-03
BEIJING UNIV OF TECH
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AI Technical Summary

Problems solved by technology

Aiming at the characteristics of strong temporal-spatial correlation of video data, some algorithms add LSTM structure to the autoencoder to enhance the temporal modeling ability of the algorithm, but this method of extracting features and then performing temporal modeling still ignores many Spatio-Temporal Characteristics of Temporal Video Data

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  • A video anomaly detection method based on ST-Unet
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  • A video anomaly detection method based on ST-Unet

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Embodiment Construction

[0033] The specific implementation method of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0034] 1. Pretreatment

[0035] The continuous long video is segmented into a single video frame image, and the segmented video frame image is input into the preprocessing network composed of a single dropout layer to obtain the preprocessed "damaged" video frame image data. The specific network structure is as figure 1 As shown, the keep_prob of the Dropout layer is set to 0.8.

[0036] 2. Build ST-Unet network

[0037] Such as figure 2 shown. The specific parameters of each layer of the ST-Unet network constructed by the present invention are as follows:

[0038] ①, C1, C2 two convolutional layers: the input size is 256×256, the number of input channels is 3, the convolution kernel is 3×3, the step size is 1, the edge filling method is 'valid', the activation function is ReLU, and the output The size is 256×256, and the ...

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Abstract

The invention discloses a video anomaly detection method for an ST-Unet network, and relates to the field of computer vision and pattern recognition. According to the invention, a new ST-Unet networkis provided for solving the processing problem of such data with strong space-time correlation as videos; and the the good modeling capability of the Unet network is not only utilized in spatial characteristics, the modeling capability of the ConvLSTM is but also combined in time. In order to improve the accuracy and the blooming capability of the algorithm, damage preprocessing is carried out oninput video data through a Dropout layer in the algorithm training process. The network obtained by training the damaged training data not only has a better anomaly detection effect on complete test data, but also can detect whether the noise-containing data in the test process is abnormal or not. According to the invention, a reconstruction algorithm result and a prediction algorithm result are combined and discriminated, so that high-precision ST-based is realized; the invention discloses a video anomaly detection algorithm of a Unet network.

Description

technical field [0001] The invention belongs to the field of computer vision and pattern recognition, and relates to a video anomaly detection method of ST-Unet network. Background technique [0002] With the continuous progress of society, the concept of safe city has gradually become one of the most concerned topics. Among them, a perfect monitoring system is a very important part of building a safe city, and video monitoring technology has become the most important means and method of security monitoring. At present, the common monitoring video processing method is still a relatively elementary monitoring method, that is, using a monitoring camera to take pictures and provide real-time display, and the supervisors will observe the monitoring video in real time, and judge whether there is any abnormal event based on experience. This monitoring method not only requires the management personnel to observe the monitoring video screen at all times, which consumes a lot of lab...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/04H04N7/18
Inventor 蔡轶珩李媛媛刘嘉琦马杰
Owner BEIJING UNIV OF TECH
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