Human body abnormal behavior detection method based on T-TINY-YOLO network

A T-TINY-YOLO, detection method technology, applied in the field of video analysis and processing, and image, can solve the problem of difficult detection of abnormal human behavior, achieve effective detection and time utilization, accelerate algorithms, and solve the effect of network redundancy

Inactive Publication Date: 2020-11-06
BEIHANG UNIV
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Problems solved by technology

[0009] Aiming at the complex and diverse detection problems of abnormal human behaviors in the existing surveillance video, the present invention proposes a detection method for abnormal human behaviors based on T-TINY-YOLO network. Improve the abnormal target detection technology, tailor the network around the real-time aspect; at the same time, transplant it to the embedded hardware platform TX2 and run it successfully, and get accurate extraction results

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  • Human body abnormal behavior detection method based on T-TINY-YOLO network
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  • Human body abnormal behavior detection method based on T-TINY-YOLO network

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[0037] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be further described in detail and in-depth below in conjunction with the accompanying drawings.

[0038] The research of the abnormal target detection algorithm is: how to let the computer automatically find out the location of the action in the video sequence and identify the category of the action. The present invention proposes a human body abnormal behavior detection method based on the T-TINY-YOLO network. First, according to different monitoring scenarios, the abnormal behavior is defined and marked, and then the improved T-TINY-YOLO network model is trained with the simply calibrated abnormal behavior. It does not extract and classify abnormal human targets, nor does it use the extraction, identification and analysis of human key points, but directly puts them into the neural network, so as to realize end-to-end abnormal behavior...

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Abstract

The invention discloses a human body abnormal behavior detection method based on a T-TINY-YOLO network, and belongs to the technical field of image and video analysis and processing in computer vision. The method comprises the following specific steps: firstly, according to different monitoring scenes, selecting a video sequence within a period of time, converting the video sequence into a picture, storing and preprocessing the picture; labeling four human abnormal behaviors which comprises finger, push, hug and standing in each picture by using a labeling tool, and generating an xml file as adata set; then, dividing the data set into a training sample and a verification sample, and inputting the training sample and the verification sample into an improved T-TINY-YOLO network model for training and verification; and finally, for a new monitoring video frame picture, directly inputting the new monitoring video frame picture into the trained T-TINY-YOLO network model after preprocessing, and outputting the category to which the calibration result of the human abnormal behavior belongs, thereby realizing end-to-end abnormal behavior classification. According to the invention, the size of the network is cut, the problem of network redundancy is solved, and the algorithm is accelerated, so that the network detection and time utilization are more effective.

Description

technical field [0001] The invention relates to a T-TINY-YOLO network-based human body abnormal behavior detection method, which belongs to the technical field of image and video analysis and processing in computer vision. Background technique [0002] With the improvement of imaging equipment and the strengthening of public traffic safety awareness, people's demand for video detection and abnormal situation detection has also increased. Video detection has been widely used in various fields, such as civil anti-theft, atmospheric observation, Disaster monitoring, enemy reconnaissance, agriculture, forestry and vegetation protection, etc., which also put forward higher requirements for the processing method, speed and application level of abnormal target detection. To detect objects in video streams, it is necessary to clarify the relationship and differences between judging abnormal object detection and ordinary object detection. [0003] Traditional target detection and re...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V40/20G06V20/40G06V20/52G06N3/045G06F18/214
Inventor 丁文锐曾羡霖姜亚龙
Owner BEIHANG UNIV
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