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Active intervention monitoring method for dangerous elevator taking behaviors of escalator

An escalator and behavioral technology, applied in image data processing, biological neural network models, instruments, etc., can solve problems such as escalator error execution, panic, injury, etc.

Pending Publication Date: 2020-10-20
CHONGQING SPECIAL EQUIP INSPECTION & RES INST
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Accurate identification of children's identities is the top priority of the entire safety prevention mechanism. At the same time, whether children have unsafe behaviors such as stepping on yellow lines when riding an elevator requires real-time and accurate identification and prediction. Precise, it is likely to cause wrong execution of the escalator, it is difficult to achieve safety prevention, and even panic or secondary accidents
[0004] The invention patent "Escalator Protection System Based on Image Recognition Algorithm to Protect Children's Safety" CN105967038A mainly solves the behavior recognition and protection of children riding escalators alone. However, in reality, many accidents occur when children are accompanied by adults, and the adults do not supervise in place or look down Mobile phones, causing children to step on the yellow line when they are on the ladder and get caught in the gap between the steps and cause injury

Method used

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  • Active intervention monitoring method for dangerous elevator taking behaviors of escalator
  • Active intervention monitoring method for dangerous elevator taking behaviors of escalator
  • Active intervention monitoring method for dangerous elevator taking behaviors of escalator

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0058] Example 1: Preliminary screening of infrared thermal imaging

[0059] Infrared thermal imaging is used to monitor whether there are living organisms on the steps. If the temperature of the imaging surface is greater than 35 degrees, it indicates that there are living organisms. Then turn on the video image input monitoring. If it is not living organisms, such as non-living objects such as crimping lines of boxes will be excluded.

Embodiment 2

[0060] Embodiment 2: Child identification model construction

[0061] Described convolutional neural network comprises convolutional layer, pooling layer, activation layer, fully connected layer, and adopts cross-entropy loss function to train, training process adopts stochastic gradient descent (SGD) to optimize, forms stable child behavior recognition model ;

[0062] Among them, the cross entropy loss function:

[0063]

[0064] the y i Indicates the mark of sample i, the positive class is 1, and the negative class is 0; p i Indicates the probability that sample i is predicted to be positive; N is the number of input samples.

Embodiment 3

[0065] Example 3: Judgment of Safe Behavior

[0066] Convert the preprocessed input image into HSV color space, the model space includes: hue (H), saturation (S), and brightness (V); use the color range of the yellow warning line on the step, set the threshold for two Values ​​are used for target positioning. The yellow HSV reference ranges are as follows: [16,35], [160,255], [50,255]. The valued result is extracted to obtain the target area (ROI) of the warning line.

[0067] The above algorithm is embedded into the NVIDIA Jetson system to realize efficient offline monitoring, and the abnormal monitoring signal is input into the escalator control system for emergency control.

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Abstract

The invention belongs to the technical field of safety monitoring, and mainly relates to an active intervention monitoring method for dangerous elevator taking behaviors of an escalator. The method comprises the following steps: collecting elevator taking images in real time through camera equipment, performing preprocessing and manual marking on the images, and forming a training set together with the images; using a convolutional neural network and the constructed training set to carry out convolution processing to form a stable child behavior recognition model; converting the preprocessed images into an HSV color space, performing color positioning by using a yellow warning line on the step, and extracting a warning line target area according to a binarization result to obtain a warningline target area length L and a center mass C; judging whether the target area length L and the mass center C of the warning line are geometrically deformed and deviated; and if the length L and themass center C are geometrically deformed and deviated, forming an abnormal signal, and outputting the abnormal signal to an escalator control system. According to the method, the child identity can beaccurately recognized based on an image recognition algorithm, linkage control with the escalator system is achieved, and child safety protection is achieved.

Description

technical field [0001] The invention belongs to the technical field of safety monitoring, and in particular relates to an active intervention monitoring method for dangerous riding behaviors of escalators. Background technique [0002] Escalators are found in crowded places such as shopping malls, subways, and supermarkets. The operating environment of escalators is very complex. Passengers face not only stationary parts, but also moving parts. Relative motion is one of the important reasons for escalator accidents. Due to the steps with circular operation, it often happens that the toes are involved in the gap between adjacent steps, thereby causing injury accidents, especially children's feet are relatively small and their safety awareness is weak, which is very easy to cause injury. [0003] Yellow warning lines are generally marked on the steps of the escalator. As long as passengers stand within the yellow line of the steps, there will be no accidents involving injuries...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06T5/00G06T5/40G06T7/11G06T7/90G08B21/02
CPCG06T5/40G06T7/90G06T7/11G08B21/02G06V40/20G06N3/045G06F18/214G06T5/70Y02B50/00
Inventor 吕潇陈舒涵贾海军张莉袁旌杰康立贵张学伦
Owner CHONGQING SPECIAL EQUIP INSPECTION & RES INST