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Subway station pedestrian abnormal event detection method based on deep learning

A pedestrian detection and deep learning technology, applied in the field of video processing, can solve problems such as non-intelligence and high manpower consumption

Active Publication Date: 2019-12-03
CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

[0003] The purpose of the present invention is to disclose a method for detecting abnormal pedestrian events in subway stations based on deep learning, which can solve technical problems such as large manpower consumption and non-intelligence of traditional manual detection and monitoring

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  • Subway station pedestrian abnormal event detection method based on deep learning
  • Subway station pedestrian abnormal event detection method based on deep learning
  • Subway station pedestrian abnormal event detection method based on deep learning

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Embodiment Construction

[0089] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0090] The present invention provides a method for detecting abnormal pedestrian events in subway stations based on deep learning, comprising the following steps:

[0091] Step 1. Integrate and process the four data sets of VOC2007, VOC2012, CVC09, and CVC14 to obtain a pedestrian detection data set containing 22139 image samples;

[0092] Specifically, the four datasets can be downloaded from the Internet. Among them, the VOC dataset is a target detection dat...

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Abstract

The invention discloses a subway station pedestrian abnormal event detection method based on deep learning. According to the method, the most advanced detection and tracking algorithm in deep learningis adopted; the method can be used for automatic real-time detection of pedestrian abnormity in a subway station. Pictures describing pedestrians are made into a pedestrian detection data set and sent to a deep residual convolutional neural network for training. A pedestrian detection model is obtained, a pedestrian tracking algorithm Deepsort is used for completing pedestrian tracking with a detection result as input, the tracking result is further processed, and detection and judgment of specific pedestrian abnormal behaviors are completed in combination with a warning line detection result. The method has the advantages that the detection precision is high, the speed reaches 15FPS, and the real-time requirement of monitoring can be met.

Description

【Technical field】 [0001] The invention relates to the technical field of video processing, in particular to a method for detecting abnormal pedestrian events in subway stations based on deep learning. 【Background technique】 [0002] Subway transportation has become an extremely important mode of public transportation today, and plays an extremely important role in the development of the national economy. At present, major cities are actively developing and constructing subway transportation. In daily life, people will also be more choose the subway. Compared with other modes of transportation, subway transportation has the advantages of large transportation volume, fast speed, saving ground space, environmental protection and energy saving, etc., but at the same time, subway operation also has its own particularities. Areas such as escalators are generally one-way traffic, and once passengers have relevant abnormal behaviors, if the staff cannot detect them in time and take...

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/08G06V40/20G06V20/41G06V20/44G06N3/045
Inventor 张辉裴宇李树涛钟杭刘理邓广李玲
Owner CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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