CN113221612A - Visual intelligent pedestrian monitoring system and method based on Internet of things - Google Patents
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Abstract
The invention discloses a visual intelligent pedestrian monitoring system and method based on the Internet of things.A first camera identifies non-database pedestrians, namely unknown pedestrians, in a monitoring video; detecting the total number of pedestrians in the monitoring video by the second camera; the information interaction terminal, the gateway and the server realize storage and transmission of pedestrian information; the user terminal is used for a user to acquire real-time pedestrian information or send an instruction to the server; the wireless communication device is connected with the gateway, and after the instruction sent by the Internet of things receiving terminal is sent, the instruction is sent to the wireless communication device through the gateway of the successive server, so that the function of alarming or pedestrian information and purpose confirmation through communication with the pedestrian is achieved in real time. The method has the advantages of intellectualization, real-time performance and low cost.
Description
Technical Field
The invention belongs to the technical field of monitoring of the Internet of things, and particularly relates to a visual intelligent pedestrian monitoring system and method based on the Internet of things.
Background
The internet of things is the third revolution of the information technology industry. The internet of things refers to the fact that any object is connected with a network through information sensing equipment according to an agreed protocol, information exchange and communication are conducted on the object through an information transmission medium, so that functions of intelligent identification, positioning, tracking, supervision and the like are achieved, and the internet of things technology represents the future development direction of the internet.
The concept of deep learning was proposed in 2006 by Hinton et al, and originated from the study of artificial neural networks: interconnection between neurons. Deep learning is widely applied to other fields such as computer vision, speech recognition, natural language processing and the like by combining low-level features to form more abstract high-level representation attribute categories or features so as to find distributed feature representations of data.
With the improvement of the performance of computer hardware, the pedestrian recognition technology based on the deep neural network has the interests of researchers and scholars again, and the research of deep learning also becomes the research hotspot of the computer vision at present. The research relates to a plurality of research fields such as image processing, computer vision, machine learning, image retrieval and the like, has important scientific significance, can be widely applied to the field of computer application, such as intelligent security, security and the like, and has good application prospect.
In the age of self-renewal and self-upgrade of information, networks, wireless mobile communication technologies and network technologies at an alarming rate, 5G (fifth generation mobile communication technology), which is the latest generation cellular mobile communication technology, represents the latest development direction of wireless mobile communication technologies, and has been widely researched, developed and put into practical use in recent years, regardless of scientific research or practical application.
Disclosure of Invention
The invention aims to solve the technical problem of providing a visual intelligent pedestrian monitoring system and method based on the Internet of things, aiming at the defects of the prior art.
In order to achieve the technical purpose, the technical scheme adopted by the invention is as follows:
a visual intelligent pedestrian monitoring system based on the Internet of things comprises a first camera, a second camera, an information interaction end, a gateway, a server, a user terminal and a wireless communication device;
the first camera identifies non-database pedestrians, namely unknown pedestrians, in the monitoring video;
detecting the total number of pedestrians in the monitoring video by the second camera;
the information interaction terminal, the gateway and the server realize storage and transmission of pedestrian information;
the user terminal is used for a user to acquire real-time pedestrian information or send an instruction to the server;
the wireless communication device is connected with the gateway, and after the instruction sent by the Internet of things receiving terminal is sent, the instruction is sent to the wireless communication device through the gateway of the successive server, so that the function of alarming or pedestrian information and purpose confirmation through communication with the pedestrian is achieved in real time.
In order to optimize the technical scheme, the specific measures adopted further comprise:
the information interaction terminal is communicated with the gateway through ZigBee or mMTC.
The gateway adopts WiFi to perform handshake connection with the server and adopts an EDP protocol to perform bidirectional communication.
A visual intelligent pedestrian monitoring method based on the Internet of things comprises the following steps:
step 1, a first camera identifies a person image in a monitoring video, and compares the identified person image with a pedestrian photo database to obtain a non-database pedestrian;
step 2, detecting the total number of pedestrians in the monitoring video by a second camera;
step 3, calculating the number of non-database pedestrians, namely unknown pedestrians;
and 4, transmitting the unknown pedestrian number and the unknown pedestrian image to the user terminal through the information interaction terminal, the gateway and the server in sequence.
The second camera in the step 2 detects the total number of pedestrians in the monitoring video, and specifically includes:
the second camera scans the number of pedestrians in the monitoring video by using a Faster R-CNN algorithm.
The second camera scans the number of pedestrians in the monitoring video by using a Faster R-CNN algorithm, namely, the pedestrian re-identification comprises the following steps:
step 21, inputting an image;
step 22, generating a candidate region through the region generation network RPN;
step 23, extracting features;
step 24, classifying by a classifier;
and 25, returning by the regressor and adjusting the position.
The first camera in the step 1 identifies the person image in the monitoring video, and compares the identified person image with the pedestrian photo database to obtain the non-database pedestrian, which specifically comprises:
and training a pedestrian photo database by adopting a machine learning decision tree algorithm, and then identifying the faces of pedestrians in the monitoring video to obtain non-database pedestrians, namely unknown pedestrians.
The invention has the following beneficial effects:
1. the intelligent monitoring is realized, the artificial real-time monitoring is not needed, and the intelligent monitoring system can be used continuously for 24 hours.
2. The real-time property can be directly realized by re-identifying pedestrians through videos collected by the camera, then information is sent to the gateway of the Internet of things, the protocol can use the traditional ZigBee or conform to the trend of 5G, and the mMTC is used.
3. The cost is low, and basically no extra expenditure is needed except for the price of the camera.
4. And improving a neural network algorithm, and using the Faster R-CNN for pedestrian re-identification.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
fig. 2 is a schematic diagram of a pedestrian re-identification process.
Detailed Description
Embodiments of the present invention are described in further detail below with reference to the accompanying drawings.
The invention discloses a visual intelligent pedestrian monitoring system based on the Internet of things, which comprises a first camera, a second camera, an information interaction end, a gateway, a server, a user terminal and a wireless communication device, wherein the first camera is connected with the second camera;
the first camera identifies non-database pedestrians, namely unknown pedestrians, in the monitoring video;
detecting the total number of pedestrians in the monitoring video by the second camera;
the information interaction terminal, the gateway and the server realize storage and transmission of pedestrian information;
the user terminal is used for a user to acquire real-time pedestrian information or send an instruction to the server;
the wireless communication device is connected with the gateway, and after the instruction sent by the Internet of things receiving terminal is sent, the instruction is sent to the wireless communication device through the gateway of the successive server, so that the function of alarming or pedestrian information and purpose confirmation through communication with the pedestrian is achieved in real time.
In the embodiment, the information interaction terminal communicates with the gateway through ZigBee or mMTC.
In the embodiment, the gateway adopts WiFi to perform handshake connection with the server and adopts an EDP protocol to perform bidirectional communication.
Referring to fig. 1, a visual intelligent pedestrian monitoring method based on internet of things includes:
step 1, a first camera identifies a person image in a monitoring video, and compares the identified person image with a pedestrian photo database to obtain a non-database pedestrian;
step 2, detecting the total number of pedestrians in the monitoring video by a second camera;
step 3, calculating the number of non-database pedestrians, namely unknown pedestrians;
and 4, transmitting the unknown pedestrian number and the unknown pedestrian image to the user terminal through the information interaction terminal, the gateway and the server in sequence.
In an embodiment, the first camera in step 1 identifies a person image in the surveillance video, and compares the identified person image with a pedestrian photo database to obtain a non-database pedestrian, specifically:
and training a pedestrian photo database by adopting a machine learning decision tree algorithm, and then identifying the faces of pedestrians in the monitoring video to obtain non-database pedestrians, namely unknown pedestrians. (pedestrian face recognition)
In an embodiment, the second camera in step 2 detects the total number of pedestrians in the monitoring video, specifically:
referring to fig. 2, the second camera scans the number of pedestrians in the surveillance video by using the fast R-CNN algorithm, which includes the following steps:
the four steps are all given to a deep neural network and are all operated on a GPU, so that the operation efficiency is greatly improved; the fast RCNN can be said to consist of two modules: a region generation network RPN candidate frame extraction module + Fast RCNN detection module; the RPN is a full convolutional neural network, and its inside is different from a general convolutional neural network in that a full link layer in the CNN is changed into a convolutional layer. Fast RCNN is based on RPN-extracted propofol detection and identification of targets in propofol; the specific process can be roughly summarized as follows: 1. an image is input. 2. Candidate regions are generated by the region generation network RPN. 3. And (5) extracting features. 4. And classifying by a classifier. 5. The regressor regresses and adjusts the position.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.
Claims (7)
1. A visual intelligent pedestrian monitoring system based on the Internet of things is characterized by comprising a first camera, a second camera, an information interaction end, a gateway, a server, a user terminal and a wireless communication device;
the first camera identifies non-database pedestrians, namely unknown pedestrians, in the monitoring video;
detecting the total number of pedestrians in the monitoring video by the second camera;
the information interaction terminal, the gateway and the server realize storage and transmission of pedestrian information;
the user terminal is used for a user to acquire real-time pedestrian information or send an instruction to the server;
the wireless communication device is connected with the gateway, and after the instruction sent by the Internet of things receiving terminal is sent, the instruction is sent to the wireless communication device through the gateway of the successive server, so that the function of alarming or pedestrian information and purpose confirmation through communication with the pedestrian is achieved in real time.
2. The visual intelligent pedestrian monitoring system based on the Internet of things of claim 1, wherein the information interaction terminal is in communication with a gateway through ZigBee or mMTC.
3. The visual intelligent pedestrian monitoring system based on the Internet of things of claim 1, wherein the gateway adopts WiFi to perform handshaking connection with the server and adopts EDP protocol to perform bidirectional communication.
4. The pedestrian monitoring method of the visual intelligent pedestrian monitoring system based on the Internet of things as claimed in any one of claims 1 to 3, wherein the method comprises the following steps:
step 1, a first camera identifies a person image in a monitoring video, and compares the identified person image with a pedestrian photo database to obtain a non-database pedestrian;
step 2, detecting the total number of pedestrians in the monitoring video by a second camera;
step 3, calculating the number of non-database pedestrians, namely unknown pedestrians;
and 4, transmitting the unknown pedestrian number and the unknown pedestrian image to the user terminal through the information interaction terminal, the gateway and the server in sequence.
5. The visual intelligent pedestrian monitoring method based on the internet of things as claimed in claim 4, wherein the step 2 of detecting the total number of pedestrians in the monitoring video by the second camera specifically comprises:
the second camera scans the number of pedestrians in the monitoring video by using a Faster R-CNN algorithm.
6. The visual intelligent pedestrian monitoring method based on the internet of things as claimed in claim 5, wherein the second camera scans the number of pedestrians in the monitored video by using the fast R-CNN algorithm, namely the pedestrian re-identification, and comprises the following steps:
step 21, inputting an image;
step 22, generating a candidate region through the region generation network RPN;
step 23, extracting features;
step 24, classifying by a classifier;
and 25, returning by the regressor and adjusting the position.
7. The method as claimed in claim 4, wherein the step 1 of identifying the person image in the surveillance video by the first camera, comparing the identified person image with a pedestrian photo database to obtain the non-database pedestrian, specifically:
and training a pedestrian photo database by adopting a machine learning decision tree algorithm, and then identifying the faces of pedestrians in the monitoring video to obtain non-database pedestrians, namely unknown pedestrians.
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