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Video abnormal behavior discrimination method based on non-local network deep learning

A technology of network depth and discrimination method, applied in computer parts, character and pattern recognition, instruments, etc., can solve the problems of difficult data collection and labeling, low frequency of abnormal events, etc.

Active Publication Date: 2019-08-02
SOUTHEAST UNIV
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  • Abstract
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Problems solved by technology

[0004]The anomaly detection task faces several major difficulties: the frequency of abnormal events is very low, which makes data collection and labeling more difficult; Positive samples are far less than negative samples; in monitoring scenarios, both normal and abnormal events are diverse and complex, that is, the diversity within categories is high

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  • Video abnormal behavior discrimination method based on non-local network deep learning
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  • Video abnormal behavior discrimination method based on non-local network deep learning

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Embodiment Construction

[0044] Below in conjunction with accompanying drawing and specific embodiment the present invention is described in further detail:

[0045]The invention provides a video abnormal behavior discrimination method based on non-local network deep learning, and applies the multi-instance method to an NL-I3D network combining non-local connection network blocks and I3D networks to classify videos.

[0046] Taking the public data set UCSD as an example, the specific implementation of the video abnormal behavior discrimination method based on non-local network deep learning of the present invention will be further described in detail in conjunction with the accompanying drawings. The overall process is shown in the appendix figure 1 As shown, some samples of the UCSD dataset are shown in the appendix figure 2 As shown, the overall network structure is shown in the appendix image 3 shown.

[0047] Step 1: Divide the video into positive and negative packets, and divide them equally ...

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Abstract

The invention discloses a video abnormal behavior discrimination method based on non-local network deep learning, and belongs to the field of computer vision, intelligence and multimedia signal processing. A training set is constructed by using a multi-example learning idea, and positive and negative packages and examples of video data are defined and labeled. A non-local network is adopted for feature extraction of a video sample, an I3D network of a residual structure serves as a convolution filter for extracting space-time information, and a non-local network block fuses long-distance dependence information so as to meet the time sequence and space requirements of video feature extraction. After the features are obtained, a regression task is established through a weakly supervised learning method, and a model is trained. According to the method, unlabeled categories can be judged, and the method is suitable for the conditions that positive samples of abnormal detection tasks are rare and the diversity in the categories is high. The method meets the recall rate requirement of an abnormal scene and has engineering application value.

Description

technical field [0001] The invention relates to the fields of computer vision, artificial intelligence, and multimedia signal processing, in particular to a video abnormal behavior discrimination method based on non-local network deep learning. Background technique [0002] Behavior and action recognition is a very important field in the discipline of computer vision, which has extremely high academic research value and commercial application value. The main goal of video behavior recognition is to determine the classification labels of the actions in the video clips, such as running, jumping, and playing the piano. Video behavior recognition is popular in many fields, and its application scenarios include video surveillance, motion recognition, retrieval, anomaly detection, etc. Related research on video behavior recognition includes timing behavior detection for long videos, online behavior detection for unfinished behaviors, and semantic analysis for scenes, etc. [000...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/20G06V20/40G06V20/52G06F18/214
Inventor 杨绿溪赵清玄常颖徐煜耀郑亚茹
Owner SOUTHEAST UNIV
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