CN111291632A - Pedestrian state detection method, device and equipment - Google Patents
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Abstract
The invention discloses a pedestrian state detection method, a pedestrian state detection device and pedestrian state detection equipment, which comprise the following steps: acquiring an existing pedestrian image; establishing a pedestrian state detection model, and processing the existing pedestrian image by using the pedestrian state detection model; inputting the processed pedestrian image into a pedestrian state detection model for training to obtain a trained pedestrian state detection model; and inputting the real-time pedestrian image into the trained pedestrian state detection model, and outputting a judgment result of the pedestrian state by the pedestrian state detection model. According to the pedestrian state detection method, the pedestrian state detection model is established, and the acquired human body detection image, the acquired face attribute detection image and the acquired pedestrian attribute detection image are input into the pedestrian state detection model, so that the pedestrian state detection model can comprehensively judge the state information of the pedestrian, the defect that the comprehensive condition of the pedestrian cannot be judged in the conventional pedestrian state detection method is overcome, and the pedestrian state detection method has important significance in practical application.
Description
Technical Field
The invention relates to the technical field of pedestrian detection, in particular to a pedestrian state detection method, a pedestrian state detection device and pedestrian state detection equipment.
Background
With the development of science and technology, people can recognize various information in images, and at present, the image recognition is also applied to the security protection field in a large scale, and the state information of passers-by, security doors, gates, gateways and the like are obtained through the images. However, in the prior art, the state information of the pedestrians passing through the image acquisition can only determine the number of the pedestrians, and whether the pedestrians carry luggage, children and the age state of the pedestrians can not be comprehensively detected, so that when the pedestrians pass through the security inspection channel, the working personnel can not guide the pedestrians, and the pedestrians can not be reminded to put the luggage into the luggage belt or provide a priority channel for the elder.
In summary, the detection method for the pedestrian state in the prior art has the defect that the comprehensive condition of the pedestrian cannot be judged.
Disclosure of Invention
The invention provides a pedestrian state detection method, a pedestrian state detection device and pedestrian state detection equipment, and solves the problem that the pedestrian comprehensive condition cannot be judged in the detection method for the pedestrian state in the prior art.
The invention provides a pedestrian state detection method, which comprises the following steps:
step S1: acquiring an existing pedestrian image;
step S2: establishing a pedestrian state detection model, and processing the existing pedestrian image by using the pedestrian state detection model;
step S3: inputting the processed pedestrian image into a pedestrian state detection model for training to obtain a trained pedestrian state detection model;
step S4: and inputting the real-time pedestrian image into the trained pedestrian state detection model, and outputting a judgment result of the pedestrian state by the pedestrian state detection model.
Preferably, step S2 specifically includes the following steps:
step S201: inputting the pedestrian image into a pedestrian state detection model, and detecting a human body in the pedestrian image by the pedestrian state detection model to obtain a human body detection image;
step S202: the pedestrian state detection model normalizes the human detection image to obtain a pedestrian attribute detection image;
step S203: the pedestrian state detection model carries out face detection on the human body detection image and aligns the human body detection image to obtain a human face attribute detection image.
Preferably, the attribute labeling is respectively performed on the human body detection image, the human face attribute detection image and the pedestrian attribute detection image.
Preferably, the attribute of the human body detection image is the number of pedestrians; the attribute classes of the face attribute detection image include: gender, age, ethnicity, expression, facial pose, and facial wear; the attribute categories of the pedestrian attribute detection image include: backpacks, handbags, satchels, luggage, carrying children, wheelchairs, and strollers.
Preferably, in step S3, the method specifically includes the following steps:
step S301: dividing a human body detection image, a human face attribute detection image and a pedestrian attribute detection image into a training set and a test set according to a certain proportion;
step S302: inputting a training set of human body detection images, a training set of face attribute detection images and a training set of pedestrian attribute detection images into a pedestrian state detection model in sequence to optimize weight parameters of the pedestrian state detection model;
step S303: and inputting the test set into the trained pedestrian state detection model to obtain the optimal weight parameters of the pedestrian state detection model.
Preferably, the attributes of each human face attribute detection image are classified into a human face attribute detection training set of the human face attribute detection images, and the attributes of each pedestrian attribute detection image are classified into a pedestrian attribute training set of the pedestrian attribute detection images.
Preferably, in step S301, the human body detection image is used as a positive sample of the human body detection training set, and the image without a pedestrian in the existing image is used as a negative sample of the human body detection training set and is input into the pedestrian state detection model, wherein the human body attribute detection training set includes all the classes of the attributes of the human body attribute detection images and the number of each class is equal, and the pedestrian attribute training set includes all the classes of the pedestrian attribute detection images and the number of each class is equal.
Preferably, the pedestrian state detection model comprises a human body detection model, a human face attribute detection model and a pedestrian attribute detection model, the human face attribute detection model and the pedestrian attribute detection model are formed by a convolutional neural network, at least one anti-convolutional layer and at least one initiation layer are arranged between a first convolutional layer and a last convolutional layer of the convolutional neural network, and a plurality of convolutional kernels with different sizes are adopted when the initiation is convolved; the human body detection model adopts a faceboxes algorithm.
A pedestrian state detection device comprises a pedestrian image acquisition module, an image preprocessing module and a pedestrian state detection model module;
the pedestrian image acquisition module is used for acquiring an image of a pedestrian;
the image preprocessing module is used for preprocessing the image of the pedestrian;
the pedestrian state detection model module is used for establishing a pedestrian state detection model and outputting a pedestrian state detection result.
A pedestrian state detection apparatus, the apparatus comprising a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute a pedestrian condition detection method according to any one of claims 1 to 8 in accordance with instructions in the program code.
According to the technical scheme, the invention has the following advantages:
according to the embodiment of the invention, the pedestrian state detection model is established, and the acquired human body detection image, the acquired face attribute detection image and the acquired pedestrian attribute detection image are input into the pedestrian state detection model, so that the pedestrian state detection model can comprehensively judge the state information of the pedestrian, the defect that the comprehensive condition of the pedestrian cannot be judged in the detection method for the pedestrian state in the prior art is solved, the guidance of the pedestrian passing through the security inspection channel by security inspection personnel is facilitated, and the pedestrian detection method has important significance in practical application.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a flowchart of a pedestrian state detection method, apparatus and device according to an embodiment of the present invention.
Fig. 2 is a device frame diagram of a pedestrian state detection method, device and apparatus according to an embodiment of the present invention.
Fig. 3 is a device frame diagram of a pedestrian state detection method, device and apparatus according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a pedestrian state detection method, a pedestrian state detection device and pedestrian state detection equipment, which are used for solving the technical problem that the comprehensive condition of a pedestrian cannot be judged in the pedestrian state detection method in the prior art.
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Referring to fig. 1, fig. 1 is a flowchart illustrating a method, an apparatus and a device for detecting a pedestrian state according to an embodiment of the present invention.
As shown in fig. 1, the pedestrian state detection method provided by the invention comprises the following steps:
step S1: acquiring an existing pedestrian image;
the existing pedestrian images can be selected from historical images obtained by shooting through a camera of a security inspection ramp, and the pedestrian images in the security inspection ramp cover most types of pedestrian state images, so that the subsequent learning and detection of pedestrian detection models on the pedestrian images are facilitated.
Step S2: establishing a pedestrian state detection model, and processing the existing pedestrian image by using the pedestrian state detection model;
step S3: inputting the processed pedestrian image into a pedestrian state detection model for training, and optimizing the pedestrian state detection model to obtain a trained pedestrian state detection model;
inputting the processed existing pedestrian images into a pedestrian state detection model for training, wherein the pedestrian state detection model detects and identifies images of different types and images of the same type and different attribute types to optimize the pedestrian state detection model, so that the trained pedestrian state detection model is obtained.
Step S4: and inputting the real-time pedestrian image into the trained pedestrian state detection model, and outputting a judgment result of the state of the pedestrian by the pedestrian state detection model.
Real-time pedestrian images are input into the trained pedestrian state detection model, and the pedestrian state detection model can directly detect and judge the real-time pedestrian images, so that the comprehensive state of pedestrians is directly judged.
As a preferred embodiment, step S2 specifically includes the following steps:
step S201: inputting the pedestrian image into a pedestrian state detection model, and detecting a human body in the pedestrian image by the pedestrian state detection model to obtain a human body detection image;
step S202: the pedestrian state detection model normalizes the human detection image to obtain a pedestrian attribute detection image;
step S203: the pedestrian state detection model carries out face detection on the human body detection image and aligns the human body detection image to obtain a human face attribute detection image.
The existing pedestrian image is divided into the human body detection image, so that the pedestrian state detection model only focuses on the human body in the image; the existing pedestrian image is divided into the face attribute detection image, so that the pedestrian state detection model only focuses on the face in the image; the existing pedestrian image is divided into the pedestrian attribute detection image, so that the pedestrian state detection model only focuses on the attribute of the pedestrian; the existing pedestrian images are divided into different categories, so that the pedestrian state detection model can focus on only a specific point in the images, and the learning efficiency of the pedestrian state detection model can be improved.
The pedestrian state detection model detects a pedestrian image to obtain a sub-image formed by a human body matrix, the sub-image is normalized to obtain a 128 × 256 human body matrix sub-image, a human face detection algorithm (namely mtcnn or faceboxes) is used for a human body matrix sub-image on the basis of the human body matrix sub-image to obtain a human face image, and the human face image is aligned to obtain a human face attribute detection image with the size of 96 × 112.
As a preferred embodiment, the attribute labeling is performed on the human body detection image, the human face attribute detection image and the pedestrian attribute detection image respectively.
By labeling attributes in the human body detection image, the human face attribute detection image and the pedestrian attribute detection image, the characteristics of different types of images needing pedestrian state detection model identification can be highlighted, interference items in the images can be reduced, and the learning speed of the pedestrian state detection model is improved.
As a preferred embodiment, the attribute of the human body detection image is the number of pedestrians; the attribute classes of the face attribute detection image include: gender, age, expression, ethnicity, facial pose, and facial wear; the attribute categories of the pedestrian attribute detection image include: backpacks, handbags, satchels, luggage, children's carrying, wheelchairs, and strollers.
Gender, age, expression, ethnicity, human face posture and human face dress are the most intuitive attributes of human face, and through learning the attributes, the pedestrian state detection model can deduce the comprehensive information of the pedestrian appearance. And the attribute of the pedestrian attribute detection image can enable the pedestrian state detection model to deduce the comprehensive information of the pedestrian appearance, so that whether the pedestrian carries a backpack or luggage or whether the pedestrian is a baby or a disabled person sitting on a wheelchair is judged, and the pedestrian is guided by the staff conveniently.
As a preferred embodiment, in step S3, the method specifically includes the following steps:
step S301: dividing images in the human body detection images, images in the human face attribute detection images and images in the pedestrian attribute detection images into a training set and a test set according to a certain proportion; the images are divided into a training set to train the pedestrian state detection model, and the testing set tests the detection effect of the pedestrian state detection model.
Step S302: inputting a training set of human body detection images, a training set of face attribute detection images and a training set of pedestrian attribute detection images into a pedestrian state detection model in sequence to optimize weight parameters of the pedestrian state detection model;
and inputting the training set in the human body detection image, the training set in the face attribute detection image and the training set in the pedestrian attribute detection image into a pedestrian state detection model in sequence, and adjusting the weight parameters of the pedestrian state detection model according to the three training sets.
Step S303: and inputting the test set into a pedestrian state detection model to obtain the optimal weight parameters of the pedestrian state detection model.
And inputting the test set in the human body detection image, the test set in the human face attribute detection image and the test set in the pedestrian attribute detection image into a trained pedestrian state detection model, and further adjusting the parameters of the pedestrian state detection model to find the optimal weight parameters of the pedestrian state detection model.
As a preferred embodiment, in step S301, the referent detection image is used as a positive sample of the human detection training set, the image without the pedestrian in the existing image is used as a negative sample of the human detection training set and is input into the pedestrian state detection model, the number of the negative samples is three times that of the positive samples, all classes including the attribute of the human face attribute detection image in the human face attribute detection training set are equal in number, all classes including the attribute of the human face attribute detection image in the human face attribute training set are equal in number, and the number of each class is equal.
As a preferred embodiment, the attribute of each face attribute detection image is classified into one category in the face attribute detection training set of the face attribute detection images, and the attribute of each pedestrian is classified into one category in the pedestrian attribute training set of the pedestrian attribute detection images.
As a preferred embodiment, the pedestrian state detection model is composed of a convolutional neural network, the pedestrian state detection model includes a human body detection model, a face attribute detection model and a pedestrian attribute detection model, the face attribute detection model and the pedestrian attribute detection model are composed of a convolutional neural network, at least one deconvolution layer and at least one interception layer are arranged between a first layer of convolutional layer and a last layer of convolutional layer of the convolutional neural network, and a plurality of convolutional kernels with different sizes are adopted when the interception is performed; the human body detection model adopts a faceboxes algorithm.
At least one deconvolution layer and at least one interception layer are arranged between the first layer of convolution layer and the last layer of convolution layer of the convolution neural network in the human face attribute detection model and the pedestrian attribute detection model, the convolution layer can enable the convolution neural network to better adapt to the change of the size of an input image, meanwhile, the diversity of characteristic images is increased, the characteristic images are fused in multiple scales, the operation amount is reduced, the size of the characteristic images can be reduced through the deconvolution layer, the characteristic images are supplemented and expanded, characteristics of higher levels can be learned, the model parameters of the network can be reduced, the pedestrian state detection model can adapt to the change of the size of the input image, and the convolution layer learns more abundant characteristics.
After a real-time pedestrian image is input to pass through a pedestrian state detection model, a human body detection model firstly detects the real-time pedestrian image to detect the number of pedestrians in the image, then shared feature sub-networks (such as a convolution layer, a deconvolution layer and an interception layer) in a convolutional neural network in a face attribute detection model and a pedestrian attribute detection model can extract attribute features of the real-time pedestrian image, for example, an all-connected layer can extract attributes of the real-time pedestrian image, and an output layer calculates the attributes of the face attribute detection image and the attributes of the pedestrian attribute detection image of the real-time pedestrian image through the features of the shared attributes to obtain the identification result of the comprehensive state of each pedestrian in the real-time pedestrian image.
Example 2
Embodiment 2 of the present application provides a face attribute recognition apparatus, which, for convenience of description, only shows the relevant parts of the present application, and as shown in fig. 2, the apparatus for detecting a pedestrian state includes a pedestrian image acquisition module 401, an image preprocessing module 402, and a pedestrian state detection model module 403;
the pedestrian image acquisition module 401 is configured to acquire an image of a pedestrian;
the image preprocessing module 402 is configured to preprocess an image of a pedestrian;
the pedestrian state detection model module 403 is configured to establish a pedestrian state detection model and output a result of the pedestrian state detection.
It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.
Example 3
As shown in fig. 3, a pedestrian state detection apparatus 50 includes a processor 500 and a memory 501;
the memory 501 is used for storing a program code 502 and transmitting the program code 502 to the processor;
the processor 500 is configured to execute the steps of one embodiment of the pedestrian state detection method described above, such as the steps S1 to S5 shown in fig. 1, according to the instructions in the program code 502. Alternatively, the processor 500 executes the computer program 502 to implement the functions of the modules/units in the device embodiments, such as the modules 401 to 403 shown in fig. 2.
Illustratively, the computer program 502 may be partitioned into one or more modules/units that are stored in the memory 501 and executed by the processor 500 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 502 in the terminal device 50. For example, the computer program 502 may be segmented to include a pedestrian image acquisition module, an image pre-processing module, and a pedestrian state detection model module;
the pedestrian image acquisition module is used for acquiring an image of a pedestrian;
the image preprocessing module is used for preprocessing the image of the pedestrian;
the pedestrian state detection model module is used for establishing a pedestrian state detection model and outputting a pedestrian state detection result.
The terminal device 50 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor 500, a memory 501. Those skilled in the art will appreciate that fig. 3 is merely an example of a terminal device 50 and does not constitute a limitation of terminal device 50 and may include more or fewer components than shown, or some components may be combined, or different components, for example, the terminal device may also include input-output devices, network access devices, buses, etc.
The Processor 500 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 501 may be an internal storage unit of the terminal device 50, such as a hard disk or a memory of the terminal device 50. The memory 501 may also be an external storage device of the terminal device 50, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 50. Further, the memory 501 may also include both an internal storage unit and an external storage device of the terminal device 50. The memory 501 is used for storing the computer program and other programs and data required by the terminal device. The memory 501 may also be used to temporarily store data that has been output or is to be output.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A pedestrian state detection method is characterized by comprising the following steps:
step S1: acquiring an existing pedestrian image;
step S2: establishing a pedestrian state detection model, and processing the existing pedestrian image by using the pedestrian state detection model;
step S3: inputting the processed pedestrian image into a pedestrian state detection model for training to obtain a trained pedestrian state detection model;
step S4: and inputting the real-time pedestrian image into the trained pedestrian state detection model, and outputting a judgment result of the pedestrian state by the pedestrian state detection model.
2. The pedestrian state detection method according to claim 1, wherein the step S2 specifically includes the steps of:
step S201: inputting the pedestrian image into a pedestrian state detection model, and detecting a human body in the pedestrian image by the pedestrian state detection model to obtain a human body detection image;
step S202: the pedestrian state detection model normalizes the human detection image to obtain a pedestrian attribute detection image;
step S203: the pedestrian state detection model carries out face detection on the human body detection image and aligns the human body detection image to obtain a human face attribute detection image.
3. The pedestrian state detection method according to claim 2, wherein attribute labeling is performed on the human detection image, the human face attribute detection image, and the pedestrian attribute detection image, respectively.
4. The pedestrian state detection method according to claim 3, wherein the attribute of the human body detection image is a pedestrian number; the attribute classes of the face attribute detection image include: gender, age, ethnicity, expression, facial pose, and facial wear; the attribute categories of the pedestrian attribute detection image include: backpacks, handbags, satchels, luggage, carrying children, wheelchairs, and strollers.
5. The pedestrian state detection method according to claim 4, wherein in step S3, the method specifically comprises the following steps:
step S301: dividing a human body detection image, a human face attribute detection image and a pedestrian attribute detection image into a training set and a test set according to a certain proportion;
step S302: inputting a training set of human body detection images, a training set of face attribute detection images and a training set of pedestrian attribute detection images into a pedestrian state detection model in sequence to optimize weight parameters of the pedestrian state detection model;
step S303: and inputting the test set into the trained pedestrian state detection model to obtain the optimal weight parameters of the pedestrian state detection model.
6. The method according to claim 5, wherein the attribute of each of the face attribute detection images is classified into one class in the face attribute detection training set of the face attribute detection images, and the attribute of each of the pedestrian attribute detection images is classified into one class in the pedestrian attribute training set of the pedestrian attribute detection images.
7. The method according to claim 6, wherein in step S301, the human body detection images are used as positive samples of a human body detection training set, images without pedestrians in the existing images are used as negative samples of the human body detection training set and input into the pedestrian state detection model, all classes containing the attributes of the human face attribute detection images in the human face attribute detection training set are equal in number, and all classes containing the attributes of the pedestrian attribute detection images in the pedestrian attribute training set are equal in number.
8. The pedestrian state detection method according to claim 1, wherein the pedestrian state detection model comprises a human body detection model, a face attribute detection model and a pedestrian attribute detection model, the face attribute detection model and the pedestrian attribute detection model are composed of a convolutional neural network, at least one deconvolution layer and at least one interception layer are arranged between a first layer of convolutional layer and a last layer of convolutional layer of the convolutional neural network, and a plurality of convolution kernels of different sizes are adopted when the interception is performed; the human body detection model adopts a faceboxes algorithm.
9. A pedestrian state detection device is characterized by comprising a pedestrian image acquisition module, an image preprocessing module and a pedestrian state detection model module;
the pedestrian image acquisition module is used for acquiring an image of a pedestrian;
the image preprocessing module is used for preprocessing the image of the pedestrian;
the pedestrian state detection model module is used for establishing a pedestrian state detection model and outputting a pedestrian state detection result.
10. A pedestrian state detection apparatus, characterized in that the apparatus comprises a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute a pedestrian condition detection method according to any one of claims 1 to 8 in accordance with instructions in the program code.
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