CN112528885A - Identification method for platform staff in intelligent zoning - Google Patents

Identification method for platform staff in intelligent zoning Download PDF

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CN112528885A
CN112528885A CN202011485426.2A CN202011485426A CN112528885A CN 112528885 A CN112528885 A CN 112528885A CN 202011485426 A CN202011485426 A CN 202011485426A CN 112528885 A CN112528885 A CN 112528885A
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pedestrian
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张祥祥
沈修平
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SHANGHAI ULUCU ELECTRONIC TECHNOLOGY CO LTD
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Abstract

The invention provides a platform worker identification method for intelligent zoning, which comprises the following steps: collecting pedestrian data; training a pedestrian classification model by using marked data and a convolutional neural network; a color filter; performing significance detection on the picture after passing through the color filter, and automatically and accurately extracting a warning line region mask I by using SOD 100K; drawing a closed polygonal warning area according to the warning line area mask I to obtain a mask II; taking the image and operation graph of the original image and the mask II to obtain a target area picture; carrying out pedestrian detection on the obtained target area picture by using a deep learning-based method yolov4, and if a pedestrian exists, obtaining a corresponding pedestrian detection frame bbox; extracting a corresponding pedestrian picture for the obtained pedestrian frame bbox; and classifying and identifying the pedestrian pictures by using a pre-trained pedestrian classification network model to obtain the identification result which is the non-worker or the type of the worker.

Description

Identification method for platform staff in intelligent zoning
Technical Field
The invention relates to an identification method for platform workers in an intelligent zoning.
Background
Non-working personnel in certain areas on a platform of a train or a high-speed rail station are forbidden to enter, and if ordinary passengers enter the areas, certain danger may exist, so that the intelligent zoning and the identification of the working personnel are very important for early warning and warning of the non-working personnel. Because the station environment is complicated, the installation angle of a monitoring camera is usually not fixed, and how to intelligently mark out a designated area without being influenced by the angle of the camera according to an actual scene and identify the types of workers in the area is challenging.
With the development and progress of computer vision technology and machine learning, the deep learning method is used for extracting and identifying the depth features of the object, and a robust result with a good effect can be realized on the detection and identification of the object.
The defects of the prior art are mainly as follows:
1. the detection of the region based on the traditional machine learning method is easily influenced by other environmental factors, such as illumination and objects with similar colors, and generates more noise;
2. the traditional feature extraction method is based on manually designed features, and the features are easily influenced by factors such as illumination, brightness and the like, so that the detection and classification identification results of personnel are inaccurate.
Disclosure of Invention
The method aims to overcome the defects of the prior art, the regional lines are roughly extracted from the platform warning lines based on colors, and then the warning lines are finely extracted based on a deep learning method so as to delineate the warning regions. By using the pedestrian detection and classification identification method of deep learning to detect and identify pedestrians in the platform area, the used object depth characteristics have robustness on environmental influence.
The specific technical scheme of the invention is as follows:
disclosed is a method for identifying platform staff in an intelligent zoning. Comprises the following steps:
step one, acquiring pedestrian data, and manually marking and classifying;
secondly, training a pedestrian classification model by using the manually marked data in the first step and a convolutional neural network;
step three, color filter: firstly, converting a picture into HSV space by BGR, and then respectively determining H, S, V value ranges according to the colors of warning lines, wherein the value ranges are as follows: 11< H <26, S >43 or S <255, V >46 or V <255, zeroing values that are not simultaneously within the value range of H, S, V;
and fourthly, performing significance Detection on the picture after passing through the color filter, wherein an algorithm used for automatically and accurately extracting the mask I of the warning line region is SOD100K (high effective Efficient Object Detection with 100K Parameters), and a lightweight network provided by the algorithm mainly comprises a feature extractor and a cross-stage fusion part and can simultaneously process features of multiple scales. The feature extractor is stacked with the intra-layer multi-scale blocks proposed by SOD100K and is divided into 4 stages, each stage having 3, 4, 6, and 4 intra-layer multi-scale blocks, respectively, according to the resolution of the feature map. The cross-stage fusion part, which is a flexible convolution module (goctcnvs) proposed by SOD100K, processes features from stages of the feature extractor to obtain high-resolution output.
The algorithm uses a novel dynamic weight attenuation scheme to reduce redundancy of feature representations, and the weight attenuation can be adjusted according to specific features of certain channels. In particular, during back propagation, the attenuation term may dynamically change according to the characteristics of certain channels. The weight update for dynamic weight decay can be represented as:
Figure BDA0002838947740000021
wherein λdIs a weight, x, of a dynamic weight decayiIs represented by wiCalculated features, and S (x)i) Is a measure of the feature, which may have multiple definitions, w, depending on the taskiIs the weight of the i-th layer,
Figure BDA0002838947740000023
is the gradient to be updated. In this algorithm, the goal is to assign weights according to features between stable channels, using global average pooling as a special passThe index of the trace, the formula, can be expressed as:
Figure BDA0002838947740000022
xithe characteristic diagram is shown, and H and W respectively represent the height and width of the characteristic diagram.
Step five, a closed polygonal warning area is drawn according to the warning line area mask I, the pixel value in the closed area is set to be 255, the pixel values outside the area are all set to be 0, and a mask II is obtained;
sixthly, taking the image and the operation image of the original image and the mask II in the fifth step to obtain a target area image;
seventhly, performing pedestrian Detection on the target area picture obtained in the sixth step by using a deep learning-based method yolov4(Optimal Speed and Accuracy of Object Detection), if a pedestrian exists, obtaining a corresponding pedestrian Detection frame bbox, wherein the yolov4 algorithm is a universal target Detection algorithm, and is mainly characterized in that the target Detection precision is high, the Speed is high, and a loss function mainly used by the algorithm is CIoU and can be expressed as:
Figure BDA0002838947740000031
wherein the content of the first and second substances,
Figure BDA0002838947740000032
Figure BDA0002838947740000033
Figure BDA0002838947740000034
b,bgtrespectively representing the center points of the anchor frame and the target frame, p (-) represents the Euclidean distance, and c represents the distance between the anchor frame and the target frameThe diagonal distance of the smallest rectangle covering both the anchor and the target box, ω, h represents the width and height of the predicted box, ω, respectivelygt,hgtRespectively the width and height of the real box.
Step eight, extracting corresponding pedestrian pictures of the pedestrian frames bbox obtained in the step seven;
and step nine, classifying and identifying the pedestrian pictures by using a pre-trained pedestrian classification network model to obtain the identification result which is the non-worker or the type of the worker.
Technical effects
The traditional machine learning method is used for zoning, and because the angle for installing the monitoring camera is not fixed, the monitoring camera is easily influenced by environmental factors and noise, so that the region extraction is inaccurate. According to the method, firstly, a color filtering method is adopted, most of other objects irrelevant to a target area are removed preliminarily, and then the target area is extracted finely by using the saliency target detection SOD 100K.
The pedestrian detection method based on the yolov4 is used for detecting pedestrians by using the pictures in the target area, the detected pedestrian pictures are classified by using the convolutional neural network, the convolutional neural network uses the depth characteristics of objects, is less influenced by environmental factors, can accurately identify the types of the pedestrians, and can give out early warning if the stay time of non-workers in the specified area reaches the set threshold.
Drawings
Fig. 1 is a schematic diagram illustrating an identification method of platform staff for intelligent zoning according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
A platform staff identification method of intelligent zoning comprises the following overall steps.
Step one, acquiring pedestrian data, and manually marking and classifying;
secondly, training a pedestrian classification model by using the manually marked data in the first step and a convolutional neural network;
step three, color filter: firstly, converting a picture into HSV space by BGR, and then respectively determining H, S, V value ranges according to the colors of warning lines, wherein the value ranges are as follows: 11< H <26, S >43 or S <255, V >46 or V <255, zeroing values that are not simultaneously within the value range of H, S, V;
and fourthly, performing significance Detection on the picture after passing through the color filter, wherein an algorithm used for automatically and accurately extracting the mask I of the warning line region is SOD100K (high effective Efficient Object Detection with 100K Parameters), and a lightweight network provided by the algorithm mainly comprises a feature extractor and a cross-stage fusion part and can simultaneously process features of multiple scales. The feature extractor is stacked with the intra-layer multi-scale blocks proposed by SOD100K and is divided into 4 stages, each stage having 3, 4, 6, and 4 intra-layer multi-scale blocks, respectively, according to the resolution of the feature map. The cross-stage fusion part, which is a flexible convolution module (goctcnvs) proposed by SOD100K, processes features from stages of the feature extractor to obtain high-resolution output.
The algorithm uses a novel dynamic weight attenuation scheme to reduce redundancy of feature representations, and the weight attenuation can be adjusted according to specific features of certain channels. In particular, during back propagation, the attenuation term may dynamically change according to the characteristics of certain channels. The weight update for dynamic weight decay can be represented as:
Figure BDA0002838947740000041
wherein λdIs a weight, x, of a dynamic weight decayiIs represented by wiCalculated features, and S (x)i) Is a measure of the feature, which may have multiple definitions, w, depending on the taskiIs the weight of the i-th layer,
Figure BDA0002838947740000043
is the gradient to be updated. At the position ofIn the algorithm, the objective is to perform weight distribution according to the features between stable channels, and the global average pooling is used as an index of a specific channel, and the formula can be expressed as:
Figure BDA0002838947740000042
xithe characteristic diagram is shown, and H and W respectively represent the height and width of the characteristic diagram.
Step five, a closed polygonal warning area is drawn according to the warning line area mask I, the pixel value in the closed area is set to be 255, the pixel values outside the area are all set to be 0, and a mask II is obtained;
sixthly, taking the image and the operation image of the original image and the mask II in the fifth step to obtain a target area image;
seventhly, performing pedestrian Detection on the target area picture obtained in the sixth step by using a deep learning-based method yolov4(Optimal Speed and Accuracy of Object Detection), if a pedestrian exists, obtaining a corresponding pedestrian Detection frame bbox, wherein the yolov4 algorithm is a universal target Detection algorithm, and is mainly characterized in that the target Detection precision is high, the Speed is high, and a loss function mainly used by the algorithm is CIoU and can be expressed as:
Figure BDA0002838947740000051
wherein the content of the first and second substances,
Figure BDA0002838947740000052
Figure BDA0002838947740000053
Figure BDA0002838947740000054
b,bgtrepresents the center points of the anchor frame and the target frame, respectively, (. rho.) represents the Euclidean distance, c represents the diagonal distance of the smallest rectangle that can cover both the anchor and the target frame, ω, h represents the width and height of the prediction frame, ω, respectivelygt,hgtRespectively the width and height of the real box.
Step eight, extracting corresponding pedestrian pictures of the pedestrian frames bbox obtained in the step seven;
and step nine, classifying and identifying the pedestrian pictures by using a pre-trained pedestrian classification network model to obtain the identification result which is the non-worker or the type of the worker.

Claims (1)

1. A platform staff identification method of intelligent zoning is characterized by comprising the following steps:
step one, acquiring pedestrian data, and manually marking and classifying;
secondly, training a pedestrian classification model by using the manually marked data in the first step and a convolutional neural network;
step three, color filter: firstly, converting a picture into HSV space by BGR, and then respectively determining H, S, V value ranges according to the colors of warning lines, wherein the value ranges are as follows: 11< H <26, S >43 or S <255, V >46 or V <255, zeroing values that are not simultaneously within the value range of H, S, V;
fourthly, performing significance Detection on the picture passing through the color filter, automatically and accurately extracting a warning line area mask I, wherein the used algorithm is SOD100K (high effective Efficient Object Detection with 100K Parameters), and the lightweight network provided by the algorithm mainly comprises a feature extractor and a cross-stage fusion part and can simultaneously process features of multiple scales; the feature extractor is stacked with the intra-layer multi-scale blocks proposed by SOD100K and divided into 4 stages according to the resolution of the feature map, each stage having 3, 4, 6 and 4 intra-layer multi-scale blocks, respectively; the cross-stage fusion part of a flexible convolution module (goctcnvs) proposed by SOD100K processes features from stages of the feature extractor to obtain high resolution output;
the algorithm uses a novel dynamic weight attenuation scheme to reduce the redundancy of feature representation, and the weight attenuation can be adjusted according to the specific features of certain channels; specifically, during back propagation, the attenuation term will dynamically change according to the characteristics of certain channels; the weight update for dynamic weight decay can be represented as:
Figure FDA0002838947730000011
wherein λdIs a weight, x, of a dynamic weight decayiIs represented by wiCalculated features, and S (x)i) Is a measure of the feature, which may have multiple definitions, w, depending on the taskiIs the weight of the i-th layer,
Figure FDA0002838947730000012
is the gradient to be updated; in this algorithm, the goal is to assign weights according to features between stable channels, using global average pooling as an indicator for a particular channel, the formula can be expressed as:
Figure FDA0002838947730000013
xirepresenting a feature map, H, W representing the height and width of the feature map, respectively;
step five, a closed polygonal warning area is drawn according to the warning line area mask I, the pixel value in the closed area is set to be 255, the pixel values outside the area are all set to be 0, and a mask II is obtained;
sixthly, taking the image and the operation image of the original image and the mask II in the fifth step to obtain a target area image;
seventhly, performing pedestrian Detection on the target area picture obtained in the sixth step by using a deep learning-based method yolov4(Optimal Speed and Accuracy of Object Detection), if a pedestrian exists, obtaining a corresponding pedestrian Detection frame bbox, wherein the yolov4 algorithm is a universal target Detection algorithm, and is mainly characterized in that the target Detection precision is high, the Speed is high, and a loss function mainly used by the algorithm is CIoU and can be expressed as:
Figure FDA0002838947730000021
wherein the content of the first and second substances,
Figure FDA0002838947730000022
Figure FDA0002838947730000023
Figure FDA0002838947730000024
b,bgtrepresents the center points of the anchor frame and the target frame, respectively, (. rho.) represents the Euclidean distance, c represents the diagonal distance of the smallest rectangle that can cover both the anchor and the target frame, ω, h represents the width and height of the prediction frame, ω, respectivelygt,hgtWidth and height of the real frame respectively;
step eight, extracting corresponding pedestrian pictures of the pedestrian frames bbox obtained in the step seven;
and step nine, classifying and identifying the pedestrian pictures by using a pre-trained pedestrian classification network model to obtain the identification result which is the non-worker or the type of the worker.
CN202011485426.2A 2020-12-16 2020-12-16 Identification method for platform staff in intelligent zoning Pending CN112528885A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114067360A (en) * 2021-11-16 2022-02-18 国网上海市电力公司 Pedestrian attribute detection method and device

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114067360A (en) * 2021-11-16 2022-02-18 国网上海市电力公司 Pedestrian attribute detection method and device

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