Method for detecting abnormal behaviors in elevator car

Abstract

The invention discloses a method for detecting abnormal behaviors in an elevator car. The method comprises: carrying out preprocessing operation on an original image of an elevator monitoring video; screening the preprocessed videos, and segmenting video segments in which people, pets and electric vehicles appear by adopting an optical flow method; inputting the screened video segments into a trained yolov3 model for recognition to obtain the number of people and the number of pets in the video, and judging whether an electric vehicle appears or not; calculating a motion history map of each frame in the screened video segment, and calculating an energy value of each frame image according to the motion history map; adaptively determining an energy threshold according to the number of peopleand the number of pets; and combining the energy value of the image with the determined adaptive energy threshold, and determining whether the electric vehicle exists, and judging whether an abnormalbehavior occurs in the video segment. And the video segments with the targets are screened out and then processed, so that the computing power consumption of the servers is reduced, the requirementson the number and configuration of the servers are reduced, and the operation cost is also reduced.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a method for detecting abnormal behavior in an elevator car. Background technique [0002] Abnormal or uncivilized behavior in the elevator car has brought great hidden dangers to the normal operation of the elevator and the safety of elevator passengers. At present, manual 24-hour on-duty methods are used for video surveillance or simply based on SVM, yolo and other algorithms to monitor the elevator. The video targets in the car are classified and monitored. However, in daily life, there are a large number of objects such as no one or pets appearing in the elevator car surveillance video in intermittent periods. Using deep learning algorithms such as yolo to perform video big data on the car video with or without the target for a long time without distinction Mining and analysis, the configuration requirements and distribution of the computing power of local servers or ...

AI Technical Summary

Problems solved by technology

However, in daily life, there are a large number of objects such as no one or pets appearing in the elevator car surveillance video in intermittent periods. Using deep learning algorithms such as yolo to perform video big data on the car video with or without the target for a long time without distinction Mining and analysis, the configuration requirements and distribution of the computing power of local servers or cloud servers are a great cost burden, especially in practical application scenarios, there will be dozens of surveillance videos in a single small community and the server needs to monitor them at the same time. When video big data mining analysis is used for monitoring, the method based on yolo's long-term indiscriminate analysis has high requirements for the number of servers and the configuration requirements for the computing power of a single server. At the same time, the power consumption and maintenance operations caused by long-term operation cost will be high

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