CN113496164A - Intelligent monitoring system and method - Google Patents
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- CN113496164A CN113496164A CN202010251597.2A CN202010251597A CN113496164A CN 113496164 A CN113496164 A CN 113496164A CN 202010251597 A CN202010251597 A CN 202010251597A CN 113496164 A CN113496164 A CN 113496164A
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Abstract
The invention belongs to the technical field of monitoring, and particularly relates to an intelligent monitoring system and method. The system comprises: the device comprises a video acquisition module for acquiring video signals; the video acquisition module is in signal connection with a video intelligent processing module for performing video intelligent processing; the video intelligent processing module is connected with a video image big database for carrying out video image big data analysis; meanwhile, the video intelligent processing module is further in signal connection with the video intelligent monitoring terminal. The intelligent monitoring system has the advantages of high intelligent degree and accurate monitoring.
Description
Technical Field
The invention belongs to the technical field of monitoring, and particularly relates to an intelligent monitoring system and method.
Background
The convolutional neural network is a deep learning model capable of automatically extracting features and sampling, and has high use value in the field of image processing; the method has the characteristics of high running speed, good adaptability, high-efficiency extraction of image characteristics, translation invariance and the like, and is suitable for image processing.
In modern society, video monitoring systems play a very important role in the field of security; nowadays, monitoring cameras are seen everywhere, and more than 2 hundred million monitoring cameras are owned all over the world according to statistics, wherein various devices which are provided with cameras and can be converted into monitoring at any time, such as mobile phones, notebooks, smart glasses and the like, are not included; along with the rapid increase of the number of the camera devices, the amount of the generated monitoring data is extremely large, and a large amount of manpower and material resources are consumed for extracting useful information from the monitoring data, and instability is caused in real-time performance and accuracy; on the other hand, because security personnel are hard to ensure long-time attention, especially when facing a plurality of camera devices, the security personnel are hard to process all monitoring data efficiently and in parallel, and it is difficult to avoid negligence of some details, which easily causes huge potential safety hazards. Therefore, a real-time monitoring threat analysis system based on deep learning is needed to solve the existing problems.
Disclosure of Invention
In view of the above, the main objective of the present invention is to provide an intelligent monitoring system and method, which have the advantages of high intelligent degree and accurate monitoring.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
an intelligent monitoring system, the system comprising: the device comprises a video acquisition module for acquiring video signals; the video acquisition module is in signal connection with a video intelligent processing module for performing video intelligent processing; the video intelligent processing module is connected with a video image big database for carrying out video image big data analysis; meanwhile, the video intelligent processing module is further in signal connection with the video intelligent monitoring terminal.
Further, the video intelligent processing module comprises: at least one recognition control machine and at least two recognition analysis machines, wherein: the recognition control machine is used for splitting a video needing image recognition into at least two video segments, distributing the video segments to at least two recognition analysis machines, and summarizing recognition results, which are returned by the recognition analysis machines and aim at the same video, so as to obtain a final recognition result;
the identification and analysis machines are consistent in structure and all comprise: the video image preprocessing module is used for extracting a video image target acquired by the video acquisition module, carrying out image preprocessing based on a neural network and constructing a video image big database;
the deep learning module is used for constructing a deep learning network, establishing a learning model and inquiring and analyzing the characteristic information of the video image target preprocessing neural network in a constructed video image big database;
the analysis module is used for detecting the position of each person or object in each frame of monitored image data by utilizing the convolution layer and the grid extraction layer based on the deep learning neural network, extracting each image from the monitored data and sending the extracted image to the video intelligent processing module for analysis;
a detection judgment module; analyzing each image based on a deep neural network, extracting abnormal features and outputting a crisis grade, setting thresholds of different grades according to the crisis grade, and if the crisis grade reaches a certain grade threshold, sending the crisis grade to the intelligent video monitoring terminal.
Further, the video acquisition unit includes high definition surveillance camera head and wearable equipment's camera.
Further, the video display unit comprises a normal display device and a wearable device.
An intelligent monitoring method, the method performing the steps of:
step 1: extracting a video image target acquired by the video acquisition module, and carrying out image preprocessing based on a neural network to construct a large video image database;
step 2: the video intelligent processing module processes the acquired video image;
and step 3: the intelligent video processing module analyzes each image based on the deep neural network, extracts abnormal features and outputs a crisis grade, thresholds of different grades are set according to the crisis grade, and if the crisis grade reaches a certain grade threshold, the crisis grade is sent to the intelligent video monitoring terminal.
Further, in step 2, the method for processing the acquired video image by the video intelligent processing module includes: splitting a video needing image recognition into at least two video segments, distributing the video segments to at least two recognition analyzers, and summarizing recognition results, which are returned by the recognition analyzers, aiming at the same video to obtain a final recognition result; extracting a video image target acquired by the video acquisition module, and carrying out image preprocessing based on a neural network to construct a large video image database; inquiring and analyzing the characteristic information of the video image target preprocessing neural network in a constructed video image big database; monitoring image data of each frame by utilizing a convolutional layer and a grid extraction layer based on a deep learning neural network, detecting the position of each person or object in the image data, extracting each image from the monitoring data, and sending the extracted image to a video intelligent processing module for analysis; analyzing each image based on a deep neural network, extracting abnormal features and outputting a crisis grade, setting thresholds of different grades according to the crisis grade, and if the crisis grade reaches a certain grade threshold, sending the crisis grade to the intelligent video monitoring terminal.
The intelligent monitoring system and the method have the following beneficial effects: by arranging the video acquisition unit, the video analysis processing unit, the video cloud processing server and the video display unit, wherein the video analysis processing unit enters threat analysis to obtain a threat analysis result after the target detection precision is improved by adopting a grid extraction layer method in a neural network based on deep learning, automatic analysis of monitoring data is realized, an alarm is given to a threat part and security personnel are reminded, the problems of poor real-time performance and accuracy caused by large monitoring data volume of the existing monitoring system and low efficiency caused by large workload of the monitoring personnel are solved, the pressure of manual analysis of the monitoring data of the security personnel is reduced, the labor cost is reduced, and the security efficiency is improved are achieved.
Drawings
Fig. 1 is a schematic system structure diagram of the intelligent monitoring system of the present invention.
Detailed Description
The method of the present invention will be described in further detail below with reference to the accompanying drawings and embodiments of the invention.
As shown in fig. 1, an intelligent monitoring system, the system comprising: the device comprises a video acquisition module for acquiring video signals; the video acquisition module is in signal connection with a video intelligent processing module for performing video intelligent processing; the video intelligent processing module is connected with a video image big database for carrying out video image big data analysis; meanwhile, the video intelligent processing module is further in signal connection with the video intelligent monitoring terminal.
Further, the video intelligent processing module comprises: at least one recognition control machine and at least two recognition analysis machines, wherein: the recognition control machine is used for splitting a video needing image recognition into at least two video segments, distributing the video segments to at least two recognition analysis machines, and summarizing recognition results, which are returned by the recognition analysis machines and aim at the same video, so as to obtain a final recognition result;
the identification and analysis machines are consistent in structure and all comprise: the video image preprocessing module is used for extracting a video image target acquired by the video acquisition module, carrying out image preprocessing based on a neural network and constructing a video image big database;
the deep learning module is used for constructing a deep learning network, establishing a learning model and inquiring and analyzing the characteristic information of the video image target preprocessing neural network in a constructed video image big database;
the analysis module is used for detecting the position of each person or object in each frame of monitored image data by utilizing the convolution layer and the grid extraction layer based on the deep learning neural network, extracting each image from the monitored data and sending the extracted image to the video intelligent processing module for analysis;
a detection judgment module; analyzing each image based on a deep neural network, extracting abnormal features and outputting a crisis grade, setting thresholds of different grades according to the crisis grade, and if the crisis grade reaches a certain grade threshold, sending the crisis grade to the intelligent video monitoring terminal.
Further, the video acquisition unit includes high definition surveillance camera head and wearable equipment's camera.
Further, the video display unit comprises a normal display device and a wearable device.
An intelligent monitoring method, the method performing the steps of:
step 1: extracting a video image target acquired by the video acquisition module, and carrying out image preprocessing based on a neural network to construct a large video image database;
step 2: the video intelligent processing module processes the acquired video image;
and step 3: the intelligent video processing module analyzes each image based on the deep neural network, extracts abnormal features and outputs a crisis grade, thresholds of different grades are set according to the crisis grade, and if the crisis grade reaches a certain grade threshold, the crisis grade is sent to the intelligent video monitoring terminal.
Further, in step 2, the method for processing the acquired video image by the video intelligent processing module includes: splitting a video needing image recognition into at least two video segments, distributing the video segments to at least two recognition analyzers, and summarizing recognition results, which are returned by the recognition analyzers, aiming at the same video to obtain a final recognition result; extracting a video image target acquired by the video acquisition module, and carrying out image preprocessing based on a neural network to construct a large video image database; inquiring and analyzing the characteristic information of the video image target preprocessing neural network in a constructed video image big database; monitoring image data of each frame by utilizing a convolutional layer and a grid extraction layer based on a deep learning neural network, detecting the position of each person or object in the image data, extracting each image from the monitoring data, and sending the extracted image to a video intelligent processing module for analysis; analyzing each image based on a deep neural network, extracting abnormal features and outputting a crisis grade, setting thresholds of different grades according to the crisis grade, and if the crisis grade reaches a certain grade threshold, sending the crisis grade to the intelligent video monitoring terminal.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process and related description of the system described above may refer to the corresponding process in the foregoing method embodiments, and will not be described herein again.
It should be noted that, the system provided in the foregoing embodiment is only illustrated by dividing the foregoing functional sub-units, and in practical applications, the foregoing functional allocation may be completed by different functional sub-units according to needs, that is, sub-units or steps in the embodiment of the present invention are further decomposed or combined, for example, the sub-units in the foregoing embodiment may be combined into one sub-unit, or may be further split into multiple sub-units, so as to complete all or part of the functions described above. The names of the sub-units and the steps involved in the embodiments of the present invention are only for distinguishing the sub-units or the steps, and are not to be construed as unduly limiting the present invention.
The terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing or implying a particular order or sequence.
The terms "comprises," "comprising," or any other similar term are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.
Claims (6)
1. An intelligent monitoring system, the system comprising: the device comprises a video acquisition module for acquiring video signals; the video acquisition module is in signal connection with a video intelligent processing module for performing video intelligent processing; the video intelligent processing module is connected with a video image big database for carrying out video image big data analysis; meanwhile, the video intelligent processing module is further in signal connection with the video intelligent monitoring terminal.
2. The intelligent monitoring system according to claim 1, wherein the video intelligent processing module comprises: at least one recognition control machine and at least two recognition analysis machines, wherein: the recognition control machine is used for splitting a video needing image recognition into at least two video segments, distributing the video segments to at least two recognition analysis machines, and summarizing recognition results, which are returned by the recognition analysis machines and aim at the same video, so as to obtain a final recognition result;
the identification and analysis machines are consistent in structure and all comprise: the video image preprocessing module is used for extracting a video image target acquired by the video acquisition module, carrying out image preprocessing based on a neural network and constructing a video image big database;
the deep learning module is used for constructing a deep learning network, establishing a learning model and inquiring and analyzing the characteristic information of the video image target preprocessing neural network in a constructed video image big database;
the analysis module is used for detecting the position of each person or object in each frame of monitored image data by utilizing the convolution layer and the grid extraction layer based on the deep learning neural network, extracting each image from the monitored data and sending the extracted image to the video intelligent processing module for analysis;
a detection judgment module; analyzing each image based on a deep neural network, extracting abnormal features and outputting a crisis grade, setting thresholds of different grades according to the crisis grade, and if the crisis grade reaches a certain grade threshold, sending the crisis grade to the intelligent video monitoring terminal.
3. The intelligent monitoring system according to claim 2, wherein the video acquisition unit comprises a high-definition surveillance camera and a camera of a wearable device.
4. The intelligent monitoring system of claim 3, wherein the video display unit comprises a normal display device and a wearable device.
5. An intelligent monitoring method, characterized in that the method performs the following steps:
step 1: extracting a video image target acquired by the video acquisition module, and carrying out image preprocessing based on a neural network to construct a large video image database;
step 2: the video intelligent processing module processes the acquired video image;
and step 3: the intelligent video processing module analyzes each image based on the deep neural network, extracts abnormal features and outputs a crisis grade, thresholds of different grades are set according to the crisis grade, and if the crisis grade reaches a certain grade threshold, the crisis grade is sent to the intelligent video monitoring terminal.
6. The intelligent monitoring method according to claim 5, wherein in the step 2, the method for processing the acquired video image by the video intelligent processing module comprises: splitting a video needing image recognition into at least two video segments, distributing the video segments to at least two recognition analyzers, and summarizing recognition results, which are returned by the recognition analyzers, aiming at the same video to obtain a final recognition result; extracting a video image target acquired by the video acquisition module, and carrying out image preprocessing based on a neural network to construct a large video image database; inquiring and analyzing the characteristic information of the video image target preprocessing neural network in a constructed video image big database; monitoring image data of each frame by utilizing a convolutional layer and a grid extraction layer based on a deep learning neural network, detecting the position of each person or object in the image data, extracting each image from the monitoring data, and sending the extracted image to a video intelligent processing module for analysis; analyzing each image based on a deep neural network, extracting abnormal features and outputting a crisis grade, setting thresholds of different grades according to the crisis grade, and if the crisis grade reaches a certain grade threshold, sending the crisis grade to the intelligent video monitoring terminal.
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Application publication date: 20211012 |