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Method for detecting abnormal behaviors in elevator car based on computer vision

A computer vision, elevator car technology, applied in the field of abnormal behavior detection in elevator cars, can solve the problems of background modeling method accuracy dependence, easy to be affected by light and so on

Active Publication Date: 2020-02-07
CHANGSHU INSTITUTE OF TECHNOLOGY
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Problems solved by technology

The accuracy of the background modeling method depends on the process of establishing the background model and is easily affected by light

Method used

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  • Method for detecting abnormal behaviors in elevator car based on computer vision
  • Method for detecting abnormal behaviors in elevator car based on computer vision
  • Method for detecting abnormal behaviors in elevator car based on computer vision

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Embodiment

[0134] Such as figure 1 As shown, the present invention proposes a method for detecting abnormal behavior in an elevator car based on computer vision. First, the ViBe algorithm combined with the frame difference method and the convolutional neural network YOLOv3 are used to detect moving objects in the video surveillance scene. Obtain the outer contour of the moving target through the ViBe algorithm combined with the inter-frame difference method, and obtain the aspect ratio information of the passenger's body entering the car on the basis of the outer contour; use the YOLOv3 neural network to analyze the human head in the elevator car Detect and obtain the location information of the head target. According to the number of heads detected by the YOLOv3 neural network, the abnormal behavior detection in the elevator is divided into two scenarios: single person, two people and more. In the case of a single person, the present invention utilizes the variation of the aspect rati...

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Abstract

The invention discloses a method for detecting abnormal behaviors in an elevator car based on computer vision, and the method comprises the steps: obtaining the external contour of a moving target through combining a ViBe algorithm of an inter-frame difference method, and obtaining the height-width ratio information of the body of a passenger in the car on the basis of the external contour; and detecting the number and the positions of the heads of the human bodies in the lift car through the YOLOv3 neural network. According to the number of people detected by the YOLOv3 neural network, the abnormal behavior detection in the car is divided into a single-person situation, a two-person situation and an above two-person situation. Under the condition of a single person, the falling behavior is detected by utilizing the change of the height-width ratio of the human body contour of the passenger and the vertical movement distance of the head of the passenger. On the basis of a target contour obtained by combining a ViBe algorithm of an inter-frame difference method in a scene of two or more persons, the average kinetic energy of passengers is calculated by utilizing a pyramid LK opticalflow algorithm, and the detection of violent infringement behaviors is realized. Video input and detection are carried out at the same time, and the effect of real-time detection is achieved.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to a method for detecting abnormal behavior in an elevator car based on computer vision. Background technique [0002] The video-based automatic abnormal behavior detection system can automatically judge the abnormal behavior of passengers in the car and send out signals according to the detection results. Abnormal behavior: Harmful behavior that occurs in the elevator car, refers to two behaviors of falling and violence in the present invention. Due to the large number of video surveillance in a certain area, manual monitoring is not only time-consuming and inefficient, but also unable to detect threats to passenger safety in time, so behavior recognition technology based on computer vision is the main research direction of abnormal behavior detection in recent years. [0003] At present, abnormal behavior detection methods can be roughly divided into three categories, namel...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06T7/149G06T7/246
CPCG06T7/149G06T7/246G06V20/20G06V20/46G06F18/214G06V20/52G06V10/82G06F18/24133
Inventor 徐本连孙振施健鲁明丽从金亮
Owner CHANGSHU INSTITUTE OF TECHNOLOGY
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