CN111553328A - Video monitoring method, system and readable storage medium based on block chain technology and deep learning - Google Patents
Video monitoring method, system and readable storage medium based on block chain technology and deep learning Download PDFInfo
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Abstract
The invention discloses a video monitoring method, a system and a readable storage medium based on a block chain technology and deep learning, wherein the method comprises the following steps: collecting video data, uploading the video data to a block chain network in real time, and storing the video data in groups; intercepting an original video data stream of which the time is X hours each time, and dividing the original video data stream into X video segments with the time duration of 1 hour, wherein X belongs to N; dividing each video segment with the time length of 1 hour into 20 video segments with the time length of 3 minutes; each video segment randomly extracts Y key image frames, Y is more than or equal to 50 and less than or equal to 180, and Y belongs to N; performing image analysis processing on the key image frame; and inputting the image analysis processing result into a trained deep convolution network for deep learning, and judging whether an abnormal phenomenon exists according to the recognition result. By utilizing the block chain technology and the deep learning, the intelligent video monitoring is realized, the processing efficiency and the safety of video monitoring data are improved, and the reliability of the video monitoring is further improved.
Description
Technical Field
The invention relates to the technical field of video monitoring, in particular to a video monitoring method and system based on a block chain technology and deep learning and a readable storage medium.
Background
Nowadays, the application of the internet of things is deep into the aspects of social life, and footprints left by the internet of things are seen everywhere from clothes and eating houses to industrial development. However, with the continuous development of scientific technology and the increasing general demand of human beings for intelligent application, the internet of things products in the current market still have a huge promotion space. For example, video monitoring equipment is widely applied to houses, streets, public entertainment places, enterprises and public institutions and other scenes, and plays an important role in improving social security and the happiness and the safety of the masses. The video monitoring picture can be checked in real time, the timely condition of the monitoring range can be known, and emergency measures of emergency events can be taken; details of historical events can also be found by reviewing stored historical surveillance videos. However, in the process, a huge amount of video data is generated without any doubt, the requirement on data processing efficiency is higher and higher, and the improvement of user intelligent experience is also great tendency; on the other hand, the video monitoring data directly relate to the privacy of people and enterprises and public institutions, and if the protection is improper, the video monitoring data can be stolen and utilized by lawbreakers; therefore, improving the security and reliability of video monitoring is also an urgent problem to be solved in the process of developing the internet of things.
Disclosure of Invention
In order to solve at least one technical problem, the invention provides a video monitoring method, a system and a readable storage medium based on block chain technology and deep learning.
In order to solve the above technical problem, a first aspect of the present invention discloses a video monitoring method based on a block chain technique and deep learning, where the method includes:
video data within a monitoring range is collected through a video monitoring unit, uploaded to a block chain network in real time and stored in groups;
intercepting an original video data stream of which the time is X hours each time, dividing the original video data stream into X video segments with the time duration of 1 hour, and generating a first video data packet, wherein X belongs to N;
each video segment with the time length of 1 hour is divided into 20 video segments with the time length of 3 minutes, and a second video data packet is generated;
randomly extracting Y key image frames from each video segment of the second video data packet to generate a first image data packet, wherein Y is more than or equal to 50 and less than or equal to 180, and Y belongs to N;
performing image analysis processing on the first image data packet to generate a second image data packet;
and inputting the second image data packet into a trained deep convolution network for deep learning, and judging whether an abnormal phenomenon exists according to a recognition result.
In this scheme, the specific steps of uploading to the block chain network in real time are as follows: and establishing corresponding video zone block chain nodes according to the specific deployment of the video monitoring unit in each monitoring area, and synchronizing the video monitoring data to the specific video zone block chain nodes of the corresponding area in real time when the video monitoring unit works normally.
In this scheme, the specific method for grouping the video data uploaded to the blockchain network is as follows: in the same video area block chain node, firstly grouping video data collected by the same video monitoring area according to different monitoring cameras; grouping video data acquired by the same monitoring camera according to the working state of the monitoring camera when acquiring the video data, and dividing the video data into dynamic video data and static video data; the same dynamic video data and static video data are grouped by specific date.
In this scheme, the specific steps of intercepting the original video data stream are as follows: after receiving a detection instruction of a video data stream, the video monitoring control center creates a video data stream demand side node, broadcasts a video data stream request to the block chain network, and starts to synchronize original video data streams of X hours on each video block chain node after receiving responses of other video block chain nodes.
In this scheme, the image analysis processing is performed on the first image data packet, and the specific steps are as follows: performing effective data detection on the first image data packet, and deleting ineffective image frames; and extracting a gray scale map, an LBP characteristic map, an HOG characteristic map and a gradient amplitude characteristic map of the effective image frame to generate a second image data packet.
In this scheme, the process of establishing the deep convolutional network is as follows:
acquiring a large number of daily video monitoring image frames as a training image set;
extracting a gray scale map, an LBP feature map, an HOG feature map and a gradient amplitude feature map of the plurality of daily video monitoring image frames;
classifying and packaging the gray-scale image, the LBP characteristic image, the HOG characteristic image and the gradient amplitude characteristic image, and inputting the classified and packaged gray-scale image, the LBP characteristic image, the HOG characteristic image and the gradient amplitude characteristic image into an initialized deep convolution network to further obtain deep convolution characteristics;
inputting the deep convolution characteristics into a classifier to further obtain a prediction result;
and analyzing and comparing according to the prediction result, adjusting initial parameters of the deep convolutional network and the classifier, and repeating the steps.
In the scheme, if the identification result shows that the image frame is abnormal, the video monitoring control center sends a short message notification instruction, the short message is sent to the mobile terminal device bound by the user, and the specific content comprises the monitoring position and the monitoring time point of the abnormal image frame; and simultaneously transmitting a second video data packet in which the abnormal image frame is located to the terminal equipment and the cloud computing unit for backup and sending an abnormal alarm instruction. And if the identification result shows that the image data packet is normal, deleting the first image data packet and the second image data packet.
The invention discloses a video monitoring system based on the block chain technology and the deep learning in a second aspect, which comprises a memory and a processor, wherein the memory comprises a video monitoring method program based on the block chain technology and the deep learning, and the video monitoring method program based on the block chain technology and the deep learning realizes the following steps when being executed by the processor:
video data within a monitoring range is collected through a video monitoring unit, uploaded to a block chain network in real time and stored in groups;
intercepting an original video data stream of which the time is X hours each time, dividing the original video data stream into X video segments with the time duration of 1 hour, and generating a first video data packet, wherein X belongs to N;
each video segment with the time length of 1 hour is divided into 20 video segments with the time length of 3 minutes, and a second video data packet is generated;
randomly extracting Y key image frames from each video segment of the second video data packet to generate a first image data packet, wherein Y is more than or equal to 50 and less than or equal to 180, and Y belongs to N;
performing image analysis processing on the first image data packet to generate a second image data packet;
and inputting the second image data packet into a trained deep convolution network for deep learning, and judging whether an abnormal phenomenon exists according to a recognition result.
In this scheme, the specific steps of uploading to the block chain network in real time are as follows: and establishing corresponding video zone block chain nodes according to the specific deployment of the video monitoring unit in each monitoring area, and synchronizing the video monitoring data to the specific video zone block chain nodes of the corresponding area in real time when the video monitoring unit works normally.
In this scheme, the specific method for grouping the video data uploaded to the blockchain network is as follows: in the same video area block chain node, firstly grouping video data collected by the same video monitoring area according to different monitoring cameras; grouping video data acquired by the same monitoring camera according to the working state of the monitoring camera when acquiring the video data, and dividing the video data into dynamic video data and static video data; the same dynamic video data and static video data are grouped by specific date.
In this scheme, the specific steps of intercepting the original video data stream are as follows: after receiving a detection instruction of a video data stream, the video monitoring control center creates a video data stream demand side node, broadcasts a video data stream request to the block chain network, and starts to synchronize original video data streams of X hours on each video block chain node after receiving responses of other video block chain nodes.
In this scheme, the image analysis processing is performed on the first image data packet, and the specific steps are as follows: performing effective data detection on the first image data packet, and deleting ineffective image frames; and extracting a gray scale map, an LBP characteristic map, an HOG characteristic map and a gradient amplitude characteristic map of the effective image frame to generate a second image data packet.
In this scheme, the process of establishing the deep convolutional network is as follows:
acquiring a large number of daily video monitoring image frames as a training image set;
extracting a gray scale map, an LBP feature map, an HOG feature map and a gradient amplitude feature map of the plurality of daily video monitoring image frames;
classifying and packaging the gray-scale image, the LBP characteristic image, the HOG characteristic image and the gradient amplitude characteristic image, and inputting the classified and packaged gray-scale image, the LBP characteristic image, the HOG characteristic image and the gradient amplitude characteristic image into an initialized deep convolution network to further obtain deep convolution characteristics;
inputting the deep convolution characteristics into a classifier to further obtain a prediction result;
and analyzing and comparing according to the prediction result, adjusting initial parameters of the deep convolutional network and the classifier, and repeating the steps.
In the scheme, if the identification result shows that the image frame is abnormal, the video monitoring control center sends a short message notification instruction, the short message is sent to the mobile terminal device bound by the user, and the specific content comprises the monitoring position and the monitoring time point of the abnormal image frame; and simultaneously transmitting a second video data packet in which the abnormal image frame is located to the terminal equipment and the cloud computing unit for backup and sending an abnormal alarm instruction. And if the identification result shows that the image data packet is normal, deleting the first image data packet and the second image data packet.
In the scheme, the video monitoring system based on the block chain technology and the deep learning comprises a video data acquisition module, a video data analysis processing module and a monitoring detection result output module.
The third aspect of the present invention discloses a computer-readable storage medium, where the computer-readable storage medium includes a video monitoring method program based on blockchain technology and deep learning of a machine, and when the video monitoring method program based on blockchain technology and deep learning is executed by a processor, the steps of the video monitoring method based on blockchain technology and deep learning are implemented.
According to the video monitoring method and system based on the block chain technology and the deep learning and the readable storage medium, intelligent video monitoring is achieved by utilizing the block chain technology and the deep learning, the processing efficiency and the safety of video monitoring data are improved, and the reliability of video monitoring is further improved.
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FIG. 1 is a flow chart of a video monitoring method based on block chain technology and deep learning according to the invention;
fig. 2 shows a block diagram of a video surveillance system based on blockchain technology and deep learning according to the present invention.
Detailed description of the invention
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
Fig. 1 shows a flow chart of a video monitoring method based on block chain technology and deep learning according to the present invention.
As shown in fig. 1, a first aspect of the present invention discloses a video monitoring method based on a block chain technique and deep learning, including:
video data within a monitoring range is collected through a video monitoring unit, uploaded to a block chain network in real time and stored in groups;
intercepting an original video data stream of which the time is X hours each time, dividing the original video data stream into X video segments with the time duration of 1 hour, and generating a first video data packet, wherein X belongs to N;
each video segment with the time length of 1 hour is divided into 20 video segments with the time length of 3 minutes, and a second video data packet is generated;
randomly extracting Y key image frames from each video segment of the second video data packet to generate a first image data packet, wherein Y is more than or equal to 50 and less than or equal to 180, and Y belongs to N;
performing image analysis processing on the first image data packet to generate a second image data packet;
and inputting the second image data packet into a trained deep convolution network for deep learning, and judging whether an abnormal phenomenon exists according to a recognition result.
It should be noted that the video monitoring unit in the present invention includes a plurality of intelligent monitoring cameras, and the intelligent monitoring cameras may not be rotatable or may be rotatable. The intelligent monitoring cameras can pertinently select corresponding installation modes, installation positions and the number of the intelligent monitoring cameras according to the space layout and the monitoring target requirements of a monitoring environment, so that a monitoring visual angle is free of blind areas, and the safety of a video monitoring area is guaranteed in an all-round mode.
It should be further noted that the specific extraction method for randomly re-extracting Y key image frames is to randomly extract Y key image frames for each video segment, extract at most one frame for each second of video, and ensure that image frames of at least 50 seconds of video are extracted according to the value range of Y.
In this scheme, the specific steps of uploading to the block chain network in real time are as follows: and establishing corresponding video zone block chain nodes according to the specific deployment of the video monitoring unit in each monitoring area, and synchronizing the video monitoring data to the specific video zone block chain nodes of the corresponding area in real time when the video monitoring unit works normally.
It should be noted that the video block chain node in the present invention may receive and store video monitoring data of at least one monitoring camera, and may also receive video monitoring data of multiple monitoring cameras at the same time, and the number of the video block chain node is mainly determined by specific requirements of monitoring tasks in an area.
In this scheme, the specific method for grouping the video data uploaded to the blockchain network is as follows: in the same video area block chain node, firstly grouping video data collected by the same video monitoring area according to different monitoring cameras; grouping video data acquired by the same monitoring camera according to the working state of the monitoring camera when acquiring the video data, and dividing the video data into dynamic video data and static video data; the same dynamic video data and static video data are grouped by specific date.
It should be noted that, in the present invention, a user may perform the following operations through a terminal device: viewing real-time monitoring video; calling and reviewing historical monitoring video data; carrying out operations such as speed doubling playing, fast forwarding, call back and the like when watching the historical monitoring video; setting timed deletion and manual deletion of historical monitoring video data.
In this scheme, the specific steps of intercepting the original video data stream are as follows: after receiving a detection instruction of a video data stream, the video monitoring control center creates a video data stream demand side node, broadcasts a video data stream request to the block chain network, and starts to synchronize original video data streams of X hours on each video block chain node after receiving responses of other video block chain nodes.
It should be noted that, the present invention can selectively set original video data streams of X hours in the past on the node of the specified video block chain in synchronization according to the requirement of the video monitoring task and the personal desire of the user.
In this scheme, the image analysis processing is performed on the first image data packet, and the specific steps are as follows: performing effective data detection on the first image data packet, and deleting ineffective image frames; and extracting a gray scale map, an LBP characteristic map, an HOG characteristic map and a gradient amplitude characteristic map of the effective image frame to generate a second image data packet.
It should be noted that, in the present invention, the effective data detection on the first image data packet is performed by performing multiple filtering operations such as classification, statistics, and screening on a large amount of image frame data according to the effective data determination standard, so as to improve the data effectiveness.
In this scheme, the process of establishing the deep convolutional network is as follows:
acquiring a large number of daily video monitoring image frames as a training image set;
extracting a gray scale map, an LBP feature map, an HOG feature map and a gradient amplitude feature map of the plurality of daily video monitoring image frames;
classifying and packaging the gray-scale image, the LBP characteristic image, the HOG characteristic image and the gradient amplitude characteristic image, and inputting the classified and packaged gray-scale image, the LBP characteristic image, the HOG characteristic image and the gradient amplitude characteristic image into an initialized deep convolution network to further obtain deep convolution characteristics;
inputting the deep convolution characteristics into a classifier to further obtain a prediction result;
and analyzing and comparing according to the prediction result, adjusting initial parameters of the deep convolutional network and the classifier, and repeating the steps.
In the scheme, if the identification result shows that the image frame is abnormal, the video monitoring control center sends a short message notification instruction, the short message is sent to the mobile terminal device bound by the user, and the specific content comprises the monitoring position and the monitoring time point of the abnormal image frame; and simultaneously transmitting a second video data packet in which the abnormal image frame is located to the terminal equipment and the cloud computing unit for backup and sending an abnormal alarm instruction. And if the identification result shows that the image data packet is normal, deleting the first image data packet and the second image data packet.
It should be noted that the abnormal alarm instruction in the present invention can be set in a grade according to the actual situation of the video monitoring environment and the actual requirements of the user, and provide corresponding emergency measures.
Fig. 2 shows a block diagram of a video surveillance system based on blockchain technology and deep learning according to the present invention.
As shown in fig. 2, a second aspect of the present invention discloses a video monitoring system based on blockchain technology and deep learning, which includes a memory and a processor, where the memory includes a video monitoring method program based on blockchain technology and deep learning, and when executed by the processor, the video monitoring method program based on blockchain technology and deep learning implements the following steps:
video data within a monitoring range is collected through a video monitoring unit, uploaded to a block chain network in real time and stored in groups;
intercepting an original video data stream of which the time is X hours each time, dividing the original video data stream into X video segments with the time duration of 1 hour, and generating a first video data packet, wherein X belongs to N;
each video segment with the time length of 1 hour is divided into 20 video segments with the time length of 3 minutes, and a second video data packet is generated;
randomly extracting Y key image frames from each video segment of the second video data packet to generate a first image data packet, wherein Y is more than or equal to 50 and less than or equal to 180, and Y belongs to N;
performing image analysis processing on the first image data packet to generate a second image data packet;
and inputting the second image data packet into a trained deep convolution network for deep learning, and judging whether an abnormal phenomenon exists according to a recognition result.
It should be noted that the system of the present invention can be operated in a terminal device such as a server, a PC, a mobile phone, a PAD, and the like.
It should be noted that the processor 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, a discrete gate or transistor logic device, a discrete hardware component, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
It should be noted that the video monitoring unit in the present invention includes a plurality of intelligent monitoring cameras, and the intelligent monitoring cameras may not be rotatable or may be rotatable. The intelligent monitoring cameras can pertinently select corresponding installation modes, installation positions and the number of the intelligent monitoring cameras according to the space layout and the monitoring target requirements of a monitoring environment, so that a monitoring visual angle is free of blind areas, and the safety of a video monitoring area is guaranteed in an all-round mode.
It should be further noted that the specific extraction method for randomly re-extracting Y key image frames is to randomly extract Y key image frames for each video segment, extract at most one frame for each second of video, and ensure that image frames of at least 50 seconds of video are extracted according to the value range of Y.
In the scheme, the video monitoring system based on the block chain technology and the deep learning comprises a video data acquisition module, a video data analysis processing module and a monitoring detection result output module.
In this scheme, the specific steps of uploading to the block chain network in real time are as follows: and establishing corresponding video zone block chain nodes according to the specific deployment of the video monitoring unit in each monitoring area, and synchronizing the video monitoring data to the specific video zone block chain nodes of the corresponding area in real time when the video monitoring unit works normally.
It should be noted that the video block chain node in the present invention may receive and store video monitoring data of at least one monitoring camera, and may also receive video monitoring data of multiple monitoring cameras at the same time, and the number of the video block chain node is mainly determined by specific requirements of monitoring tasks in an area.
In this scheme, the specific method for grouping the video data uploaded to the blockchain network is as follows: in the same video area block chain node, firstly grouping video data collected by the same video monitoring area according to different monitoring cameras; grouping video data acquired by the same monitoring camera according to the working state of the monitoring camera when acquiring the video data, and dividing the video data into dynamic video data and static video data; the same dynamic video data and static video data are grouped by specific date.
It should be noted that, in the present invention, a user may perform the following operations through a terminal device: viewing real-time monitoring video; calling and reviewing historical monitoring video data; carrying out operations such as speed doubling playing, fast forwarding, call back and the like when watching the historical monitoring video; setting timed deletion and manual deletion of historical monitoring video data.
In this scheme, the specific steps of intercepting the original video data stream are as follows: after receiving a detection instruction of a video data stream, the video monitoring control center creates a video data stream demand side node, broadcasts a video data stream request to the block chain network, and starts to synchronize original video data streams of X hours on each video block chain node after receiving responses of other video block chain nodes.
It should be noted that, the present invention can selectively set original video data streams of X hours in the past on the node of the specified video block chain in synchronization according to the requirement of the video monitoring task and the personal desire of the user.
In this scheme, the image analysis processing is performed on the first image data packet, and the specific steps are as follows: performing effective data detection on the first image data packet, and deleting ineffective image frames; and extracting a gray scale map, an LBP characteristic map, an HOG characteristic map and a gradient amplitude characteristic map of the effective image frame to generate a second image data packet.
It should be noted that, in the present invention, the effective data detection on the first image data packet is performed by performing multiple filtering operations such as classification, statistics, and screening on a large amount of image frame data according to the effective data determination standard, so as to improve the data effectiveness.
In this scheme, the process of establishing the deep convolutional network is as follows:
acquiring a large number of daily video monitoring image frames as a training image set;
extracting a gray scale map, an LBP feature map, an HOG feature map and a gradient amplitude feature map of the plurality of daily video monitoring image frames;
classifying and packaging the gray-scale image, the LBP characteristic image, the HOG characteristic image and the gradient amplitude characteristic image, and inputting the classified and packaged gray-scale image, the LBP characteristic image, the HOG characteristic image and the gradient amplitude characteristic image into an initialized deep convolution network to further obtain deep convolution characteristics;
inputting the deep convolution characteristics into a classifier to further obtain a prediction result;
and analyzing and comparing according to the prediction result, adjusting initial parameters of the deep convolutional network and the classifier, and repeating the steps.
In the scheme, if the identification result shows that the image frame is abnormal, the video monitoring control center sends a short message notification instruction, the short message is sent to the mobile terminal device bound by the user, and the specific content comprises the monitoring position and the monitoring time point of the abnormal image frame; and simultaneously transmitting a second video data packet in which the abnormal image frame is located to the terminal equipment and the cloud computing unit for backup and sending an abnormal alarm instruction. And if the identification result shows that the image data packet is normal, deleting the first image data packet and the second image data packet.
It should be noted that the abnormal alarm instruction in the present invention can be set in a grade according to the actual situation of the video monitoring environment and the actual requirements of the user, and provide corresponding emergency measures.
In a third aspect of the present invention, a computer-readable storage medium is disclosed, where the computer-readable storage medium includes a video monitoring method program based on blockchain technology and deep learning of a machine, and when the video monitoring method program based on blockchain technology and deep learning is executed by a processor, the steps of a video monitoring method based on blockchain technology and deep learning according to any one of the foregoing are implemented.
According to the video monitoring method and system based on the block chain technology and the deep learning and the readable storage medium, intelligent video monitoring is achieved by utilizing the block chain technology and the deep learning, the processing efficiency and the safety of video monitoring data are improved, and the reliability of video monitoring is further improved.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
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; can be located in one place or 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, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, 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.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several 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 methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
Claims (10)
1. A video monitoring method based on block chain technology and deep learning is characterized by comprising the following steps:
video data within a monitoring range is collected through a video monitoring unit, uploaded to a block chain network in real time and stored in groups;
intercepting an original video data stream of which the time is X hours each time, dividing the original video data stream into X video segments with the time duration of 1 hour, and generating a first video data packet, wherein X belongs to N;
each video segment with the time length of 1 hour is divided into 20 video segments with the time length of 3 minutes, and a second video data packet is generated;
randomly extracting Y key image frames from each video segment of the second video data packet to generate a first image data packet, wherein Y is more than or equal to 50 and less than or equal to 180, and Y belongs to N;
performing image analysis processing on the first image data packet to generate a second image data packet;
and inputting the second image data packet into a trained deep convolution network for deep learning, and judging whether an abnormal phenomenon exists according to a recognition result.
2. The video monitoring method based on the blockchain technology and the deep learning of claim 1, wherein the specific steps of uploading to a blockchain network in real time are as follows: and establishing corresponding video zone block chain nodes according to the specific deployment of the video monitoring unit in each monitoring area, and synchronizing the video monitoring data to the specific video zone block chain nodes of the corresponding area in real time when the video monitoring unit works normally.
3. The video monitoring method based on the blockchain technology and the deep learning of claim 1, wherein the specific method for grouping the video data uploaded to the blockchain network is as follows:
in the same video area block chain node, firstly grouping video data collected by the same video monitoring area according to different monitoring cameras; grouping video data acquired by the same monitoring camera according to the working state of the monitoring camera when acquiring the video data, and dividing the video data into dynamic video data and static video data; the same dynamic video data and static video data are grouped by specific date.
4. The video monitoring method based on the block chain technology and the deep learning as claimed in claim 1, wherein the specific step of intercepting the original video data stream is:
after receiving a detection instruction of a video data stream, the video monitoring control center creates a video data stream demand side node, broadcasts a video data stream request to the block chain network, and starts to synchronize original video data streams of X hours on each video block chain node after receiving responses of other video block chain nodes.
5. The video monitoring method based on the blockchain technology and the deep learning of claim 1, wherein the image analysis processing is performed on the first image data packet, and the specific steps are as follows: performing effective data detection on the first image data packet, and deleting ineffective image frames; and extracting a gray scale map, an LBP characteristic map, an HOG characteristic map and a gradient amplitude characteristic map of the effective image frame to generate a second image data packet.
6. The video monitoring method based on the blockchain technology and the deep learning of claim 1, wherein the deep convolutional network is established by the following steps:
acquiring a large number of daily video monitoring image frames as a training image set;
extracting a gray scale map, an LBP feature map, an HOG feature map and a gradient amplitude feature map of the plurality of daily video monitoring image frames;
classifying and packaging the gray-scale image, the LBP characteristic image, the HOG characteristic image and the gradient amplitude characteristic image, and inputting the classified and packaged gray-scale image, the LBP characteristic image, the HOG characteristic image and the gradient amplitude characteristic image into an initialized deep convolution network to further obtain deep convolution characteristics;
inputting the deep convolution characteristics into a classifier to further obtain a prediction result;
and analyzing and comparing according to the prediction result, adjusting initial parameters of the deep convolutional network and the classifier, and repeating the steps.
7. The video monitoring method based on the blockchain technology and the deep learning of claim 1, wherein:
if the identification result shows that the image frame is abnormal, the video monitoring control center sends a short message notification instruction, the short message is sent to the mobile terminal device bound by the user, and the specific content comprises the monitoring position and the monitoring time point of the abnormal image frame; and simultaneously transmitting a second video data packet in which the abnormal image frame is located to the terminal equipment and the cloud computing unit for backup and sending an abnormal alarm instruction.
And if the identification result shows that the image data packet is normal, deleting the first image data packet and the second image data packet.
8. A video monitoring system based on blockchain technology and deep learning is characterized by comprising a memory and a processor, wherein the memory comprises a video monitoring method program based on blockchain technology and deep learning, and the video monitoring method program based on blockchain technology and deep learning realizes the following steps when being executed by the processor:
video data within a monitoring range is collected through a video monitoring unit, uploaded to a block chain network in real time and stored in groups;
intercepting an original video data stream of which the time is X hours each time, dividing the original video data stream into X video segments with the time duration of 1 hour, and generating a first video data packet, wherein X belongs to N;
each video segment with the time length of 1 hour is divided into 20 video segments with the time length of 3 minutes, and a second video data packet is generated;
randomly extracting Y key image frames from each video segment of the second video data packet to generate a first image data packet, wherein Y is more than or equal to 50 and less than or equal to 180, and Y belongs to N;
performing image analysis processing on the first image data packet to generate a second image data packet;
and inputting the second image data packet into a trained deep convolution network for deep learning, and judging whether an abnormal phenomenon exists according to a recognition result.
9. The video surveillance system based on blockchain technology and deep learning of claim 8, wherein the video surveillance system based on blockchain technology and deep learning comprises a video data acquisition module, a video data analysis processing module and a monitoring detection result output module.
10. A computer-readable storage medium, wherein the computer-readable storage medium includes a program of video surveillance method based on blockchain technique and deep learning of a machine, and when the program of video surveillance method based on blockchain technique and deep learning is executed by a processor, the steps of the video surveillance method based on blockchain technique and deep learning according to any one of claims 1 to 7 are implemented.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113344470A (en) * | 2021-08-02 | 2021-09-03 | 山东炎黄工业设计有限公司 | Intelligent power supply system management method based on block chain |
| CN113660467A (en) * | 2021-08-18 | 2021-11-16 | 江西省科学院应用物理研究所 | Video monitoring system, method, equipment and medium based on block chain technology |
| CN115687023A (en) * | 2022-12-08 | 2023-02-03 | 深圳阿塔基科技有限公司 | Internet big data processing method and system |
| CN117933849A (en) * | 2024-03-21 | 2024-04-26 | 山东商业职业技术学院 | Logistics supply chain intelligent contract management system based on block chain technology |
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2020
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113344470A (en) * | 2021-08-02 | 2021-09-03 | 山东炎黄工业设计有限公司 | Intelligent power supply system management method based on block chain |
| CN113344470B (en) * | 2021-08-02 | 2021-11-16 | 山东炎黄工业设计有限公司 | Intelligent power supply system management method based on block chain |
| CN113660467A (en) * | 2021-08-18 | 2021-11-16 | 江西省科学院应用物理研究所 | Video monitoring system, method, equipment and medium based on block chain technology |
| CN115687023A (en) * | 2022-12-08 | 2023-02-03 | 深圳阿塔基科技有限公司 | Internet big data processing method and system |
| CN117933849A (en) * | 2024-03-21 | 2024-04-26 | 山东商业职业技术学院 | Logistics supply chain intelligent contract management system based on block chain technology |
| CN117933849B (en) * | 2024-03-21 | 2024-06-11 | 山东商业职业技术学院 | Logistics supply chain intelligent contract management system based on block chain technology |
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