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Multi-scale based video anomaly detection method

An anomaly detection and multi-scale technology, applied in the direction of neural learning methods, instruments, biological neural network models, etc., can solve the problems of inability to detect local anomalies, insensitivity of local anomalies, etc., to improve regional prediction capabilities, enhance local difference samples, Eliminate the effect of local prediction errors

Active Publication Date: 2021-06-25
广东众聚人工智能科技有限公司
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Problems solved by technology

We found that anomalies often occur in a certain area of ​​the video frame. If we simply calculate the peak signal-to-noise ratio score directly between the generated frame and the real frame, it will not be sensitive to local anomalies and cannot perform good detection on local anomalies. detection

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  • Multi-scale based video anomaly detection method

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Embodiment Construction

[0022] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments, wherein the schematic embodiments and descriptions are only used to explain the present invention, but are not intended to limit the present invention.

[0023] Video anomaly detection is a challenging task, and it is quite difficult to collect all types of anomalous events, which makes traditional binary classification methods unsuitable. Furthermore, it is difficult to clearly define abnormal situations. Given that outliers are usually context-dependent, abnormal events in one scene can be treated as normal events in another scene.

[0024] In the prior art, when performing anomaly detection, the anomaly type is directly found in the binary method through the training of the anomaly type, and the normal frames are input into the deep neural network during the training process of the model, and these frames are attempted to be reconstructed wi...

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Abstract

The invention relates to a multi-scale-based video anomaly detection method, and the method comprises the following steps: S1, obtaining video sample data, and carrying out the multi-scale change of a video sample; S2, constructing an anomaly detection model, and carrying out model training; And S3, testing the multi-scale anomaly detection model. According to the invention, the predicted frame and the real frame are partitioned on different scales, so that the monitoring sensitivity of local anomaly is improved.

Description

technical field [0001] The invention belongs to the field of security technology, and in particular relates to a multi-scale video anomaly detection method. Background technique [0002] Video anomaly detection refers to the detection of abnormal behaviors that occur in videos. With the increasing popularity of surveillance video, it becomes more and more necessary to automatically identify abnormal events in the video, because manual inspection may cause a lot of waste of resources (e.g., labor force). However, video anomaly detection is a challenging task due to the rarity and diversity of anomalous events. More specifically, anomalous events occur rarely and may be events that have never been seen before. Therefore, it is quite difficult to collect all types of anomalous events, which makes traditional binary classification methods unsuitable. Furthermore, it is difficult to clearly define abnormal situations. Given that outliers are usually context-dependent, abnorma...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/41G06N3/045G06F18/214
Inventor 房体品韩忠义杨光远张凯
Owner 广东众聚人工智能科技有限公司
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