Claims:
1. A intelligent video monitoring system based on deep learning techniques for automatic surveillance, characterized in that includes:
A surveillance monitoring unit is used to detect any viewing video visible within the viewing sight of the monitor and determining whether the detected viewing video belong to authorized individuals who are authorized to view video surveillance footage on the monitor or if any of the detected viewing video belong to individuals who are not authorized to view video surveillance footage on the monitor, wherein the surveillance monitoring unit comprises:
A series of visual sensor, &
A house memory storage video recorder, a walking area memory storage video recorder, a memory storage video recorder society, corridor memory storage video recorder, switch, decoder, display device wall;
A center computing unit, is used as execute programmed instructions stored in the memory for processing a set of sample video frames, using a Deep learning algorithm, to generate a training set, wherein the a training set enables detection of one or more person from a set of person, in a geographical area, wherein the set of sample video frames comprise one or more sample video frames, connected to an alert unit, visual sensor through a wireless communication are respectively connected with the house memory storage video recorder, video visual sensor and other video visual sensor through a wireless communication are respectively connected with walking area memory storage video recorder, wherein other visual sensor video visual sensor are connected through the wireless communication recess memory storage video recorder, other visual sensor video visual sensor are connected through the wireless communication corridor memory storage video recorder, house memory storage video recorder, walking area memory storage video recorder, a memory storage video recorder society, corridor memory storage video recorder, switch center computing unit through the wireless communication and are respectively connected with the switch, the alert unit is connected with the center computing unit through the wireless communication.

2. The intelligent video monitoring system based on deep learning techniques for automatic surveillance as claimed in claim 1, wherein the visual sensor parallel mobile continuous shooting multi-frame Video sequence, is calculated as the reference view with its adjacent society.
, Description:FIELD OF INVENTION

The present invention is related to of the video analysis.
The present invention relates to the field of a monitoring video person re-recognition analysis method, in particular relates to a monitoring video based on deep learning.
The present invention relates to field of video surveillance system to generate alerts for certain events. More specifically, the embodiments provide techniques allowing a behavioral recognition system.
More particularly, the present invention is related to intelligent video monitoring system based on deep learning techniques for automatic surveillance.
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BACKGROUND & PRIOR ART

The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in-and-of-themselves may also be inventions.
Known video surveillance systems often include a large number of video cameras that are used to provide video surveillance. The video surveillance may be monitored live by one or more security operators that watch video on one or more monitors for signs of suspicious behavior and other potential issues. In some cases, prior captured video may be pulled up from memory or other storage and viewed in response to a particular incident, for example. Due to privacy concerns, there may be situations in which the identity of least some individuals shown in the video surveillance needs to be anonymized. There may be privacy concerns raised as well by who is reviewing the video surveillance. While video monitoring systems are known, there is a need for improved video monitoring systems generally and video monitoring systems that meet certain privacy requirements specifically.
Some of the prior related works as follows:
CA3004002A1 VIDEO SURVEILLANCE WITH CONTEXT RECOGNITION presents “Video data from a plurality of network-connected cameras may be processed and edited so that only video data associated with a context may be provided to a device associated with a user. Machine learning algorithms may be trained, over time, to improve context recognition. The context that is recognized may relate to a particular activity in video captured by the network-connected cameras and/or an identity of a person who appears in video captured by the network-connected cameras.”
US20130243252A1 LOITERING DETECTION IN A VIDEO SURVEILLANCE SYSTEM presents “A behavioral recognition system may include both a computer vision engine and a machine learning engine configured to observe and learn patterns of behavior in video data. Certain embodiments may be configured to learn patterns of behavior consistent with a person loitering and generate alerts for same. Upon receiving information of a foreground object remaining in a scene over a threshold period of time, a loitering detection module evaluates the whether the object trajectory corresponds to a random walk. Upon determining that the trajectory does correspond, the loitering detection module generates a loitering alert.”
KR1020150084939A IMAGE STABILIZATION TECHNIQUES FOR VIDEO SURVEILLANCE SYSTEMS presents “A behavioral recognition system may include both a computer vision engine and a machine learning engine configured to observe and learn patterns of behavior in video data. Certain embodiments may provide image stabilization of a video stream obtained from a camera. An image stabilization module in the behavioral recognition system obtains a reference image from the video stream. The image stabilization module identifies alignment regions within the reference image based on the regions of the image that are dense with features. Upon determining that the tracked features of a current image are out of alignment with the reference image, the image stabilization module uses the most feature dense alignment region to estimate an affine transformation matrix to apply to the entire current image to warp the image into proper alignment. ´
EP2826020A1 ALERT VOLUME NORMALIZATION IN A VIDEO SURVEILLANCE SYSTEM presents “A behavioral recognition system may include both a computer vision engine and a machine learning engine configured to observe and learn patterns of behavior in video data. Certain embodiments may be configured to learn patterns of behavior consistent with a person loitering and generate alerts for same. Upon receiving information of a foreground object remaining in a scene over a threshold period of time, a loitering detection module evaluates the whether the object trajectory corresponds to a random walk. Upon determining that the trajectory does correspond, the loitering detection module generates a loitering alert.”
US20140055609A1 DETERMINING FOREGROUNDNESS OF AN OBJECT IN SURVEILLANCE VIDEO DATA presents “A computer identifies a proto-object in a digital image using a background subtraction method, the proto-object being associated with a lighting artifact in the surveillance region. The background subtraction method preserves boundary details and interior texture details of proto-objects associated with lighting artifacts. A plurality of characteristics of the proto-object digital data are determined, the characteristics, individually or in combination, distinguish a proto-object related to a lighting artifact from its background. A learning machine, trained with the plurality of characteristics of proto-objects classified as either foreground or not foreground, determines a likelihood that the plurality of characteristics is associated with a foreground object.”
KR101995107B1 Method and system for artificial intelligence based video surveillance using deep learning presents “an artificial intelligence based video surveillance method using deep learning and a system thereof, capable of detecting, recognizing and tracking objects based on artificial intelligent deep learning technology for an input video captured by CCTV. According to one embodiment of the present invention, the artificial intelligence based video surveillance system includes: a detection unit for detecting at least one object, which is preset for a surveillance camera image, by using a predefined detection deep learning network; a recognition unit for recognizing information about the detected object based on the predefined deep learning network and the detection result for the object; and a tracking unit for tracking the detected object based on the predefined deep learning network and the detection result for the object. The detection unit may detect the object by using region proposal extraction including a method of generating a heat map based on a convolution neural network (CNN).”
CN108764192A Multi-example multi-label learning method for video surveillance application of safe city presents “a multi-example multi-label learning method for a video surveillance application of a safe city. The invention obtains a multi-example multi-label data set of video surveillance of a safe city, and taps the internal connection between the multi-example data and the multi-tag data to predict the new video surveillance so as to determine the possible multiple security and traffic conditions implied in the area where the new video surveillance is located. The invention mainly contributes to two aspects, which firstly adopts a layered label strategy to solve the problem oaf large number of labels, thereby achieving the goal of retaining the integrity of multiple tags without losing the associated information between the labels, and secondly induces the convolutional neural network into the video surveillance network of a safe city at the first time, thereby fully deep learning the correlation between examples by taking advantage of the convolutional neural network, and fully exploring the information between the examples.”
CN105354548A Surveillance video pedestrian re-recognition method based on ImageNet retrieval presents “a surveillance video pedestrian re-recognition method based on ImageNet retrieval. The pedestrian re-recognition problem is transformed into the retrieval problem of a moving target image database so as to utilize the powerful classification ability of an ImageNet hidden layer feature. The method comprises the steps: preprocessing a surveillance video and removing a large amount of irrelevant static background videos from the video; separating out a moving target from a dynamic video frame by adopting a motion compensation frame difference method and forming a pedestrian image database and an organization index table; carrying out alignment of the size and the brightness on an image in the pedestrian image database and a target pedestrian image; training hidden features of the target pedestrian image and the image in the image database by using an ImageNet deep learning network, and performing image retrieving based on cosine distance similarity; and in a time sequence, converging the relevant videos containing recognition results into a video clip reproducing the pedestrian activity trace. The method disclosed by the present invention can better adapt to changes in lighting, perspective, gesture and scale so as to effective improve accuracy and robustness of a pedestrian recognition result in a camera-cross environment.”
KR1020180107930A Method and system for artificial intelligence based video surveillance using deep learning presents “an artificial intelligence based video surveillance method using deep learning and a system thereof, capable of detecting, recognizing and tracking objects based on artificial intelligent deep learning technology for an input video captured by CCTV. According to one embodiment of the present invention, the artificial intelligence based video surveillance system includes: a detection unit for detecting at least one object, which is preset for a surveillance camera image, by using a predefined detection deep learning network; a recognition unit for recognizing information about the detected object based on the predefined deep learning network and the detection result for the object; and a tracking unit for tracking the detected object based on the predefined deep learning network and the detection result for the object. The detection unit may detect the object by using region proposal extraction including a method of generating a heat map based on a convolution neural network”
CN108805002A Surveillance video exceptional event detection method based on deep learning and dynamic clustering presents “a surveillance video exceptional event detection method based on deep learning and dynamic clustering. In a characteristic extraction stage, a deep learning network PCA (Principal Component Analysis) Net is applied, a video is trained to learn a corresponding network filter, low-layer pixel optical flow characteristics are converted into high-layer semantic motion characteristics through a deep network, and meanwhile, motion areas in a video are screened to remove a spatial-temporal sampling block which only contains background information”
KR1020200071886A syntax-based method of providing selective video surveillance by use of deep-learning image analysis presents “a technique for effectively performing smart video control with respect to multiple CCTV cameras in general. More particularly, the present invention relates to a technique for extracting an area with a meaningful movement (that is, moving object area) in a video based on syntax information (for example, motion vector and coding type) obtained by compressed video data parsing and providing selective control in response thereto instead of detecting an object through complex image processing of the related art with respect to a compressed video generated by a CCTV camera. More particularly, the present invention relates to a technique for enhancing the effect of selective control by refining a process for extracting a moving object area based on syntax in a compressed video and filtering screen shaking or the like attributable to snow/rain, night noise, branch or banner swaying in wind, screen movement attributable to PTZ motion, and CCTV support vibration. More particularly, the present invention relates to a technique for enhancing the efficiency of video control by selecting an image of a moving object area extracted based on syntax and performing deep learning image classification instead of performing deep learning object detection with respect to a series of video frames resulting from compressed video decoding of the related art in performing video control with regard to ROI designation and object detection.”
KR102052110B1 A video surveillance apparatus for detecting agro-livestock theft based on deep learning and method thereof presents “an image monitoring technique for detecting livestock theft based on deep learning. An image monitoring method extracts a moving object for learning from an input image photographed through a camera and pre-trains each moving object for learning using image data and label data for the extracted moving object for learning. Using the pre-trained data, the moving object in a new input image is classified into livestock or human types, and a moving history of the classified moving object is recorded for each type. Each moving area is set for each type from the recorded moving history, and detects the moving object from the input image but determines an abnormal situation when the moving object is detected in an area which is not a moving area set for the classified type”
Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified, thus fulfilling the written description of all Markus groups used in the appended claims.
As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context.
The use of any and all examples, or exemplary language (e.g. “Such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.
The above information disclosed in this Background section is only for the enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art.

OBJECTIVE OF THE INVENTION

The principle objective of the present invention is to provide an intelligent video monitoring system based on deep learning techniques for automatic surveillance.

SUMMARY
Before the present systems and methods, are described, it is to be understood that this application is not limited to the particular systems, and methodologies described, as there can be multiple possible embodiments which are not expressly illustrated in the present disclosure. It is also to be understood that the terminology used in the description is for the purpose of describing the particular versions or embodiments only and is not intended to limit the scope of the present application.
The present invention mainly cures and solves the technical problems existing in the prior art. In response to these problems, the present invention discloses intelligent video monitoring system based on deep learning techniques for automatic surveillance.
As one aspect of the present invention presents an intelligent video monitoring system based on deep learning techniques for automatic surveillance, characterized in that includes
A surveillance monitoring unit is used to detect any viewing video visible within the viewing sight of the monitor and determining whether the detected viewing video belong to authorized individuals who are authorized to view video surveillance footage on the monitor or if any of the detected viewing video belong to individuals who are not authorized to view video surveillance footage on the monitor, wherein the surveillance monitoring unit comprises: a series of visual sensor , & a house memory storage video recorder, a walking area memory storage video recorder, a memory storage video recorder society, corridor memory storage video recorder, switch, decoder, display device wall, a center computing unit, is used as execute programmed instructions stored in the memory for processing a set of sample video frames, using a deep learning algorithm, to generate a training set, wherein the a training set enables detection of one or more person from a set of person, in a geographical area, wherein the set of sample video frames comprise one or more sample video frames, connected to an alert unit, visual sensor through a wireless communication are respectively connected with the house memory storage video recorder, video visual sensor and other video visual sensor through a wireless communication are respectively connected with walking area memory storage video recorder, wherein other visual sensor video visual sensor are connected through the wireless communication recess memory storage video recorder, other visual sensor video visual sensor are connected through the wireless communication corridor memory storage video recorder, house memory storage video recorder, walking area memory storage video recorder, a memory storage video recorder society, corridor memory storage video recorder, switch center computing unit through the wireless communication and are respectively connected with the switch, the alert unit is connected with the center computing unit through the wireless communication.

BRIEF DESCRIPTION OF DRAWINGS

To clarify various aspects of some example embodiments of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. It is appreciated that these drawings depict only illustrated embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail through the use of the accompanying drawings.
In order that the advantages of the present invention will be easily understood, a detailed description of the invention is discussed below in conjunction with the appended drawings, which, however, should not be considered to limit the scope of the invention to the accompanying drawings, in which:
Figure 1 shows detail block diagram representation of intelligent video monitoring system based on deep learning techniques for automatic surveillance.

DETAIL DESCRIPTION

The present invention is related to intelligent video monitoring system based on deep learning techniques for automatic surveillance.
Figure 1 shows detail block diagram representation of intelligent video monitoring system based on deep learning techniques for automatic surveillance.
Although the present disclosure has been described with the purpose of intelligent video monitoring system based on deep learning techniques for automatic surveillance, it should be appreciated that the same has been done merely to illustrate the invention in an exemplary manner and to highlight any other purpose or function for which explained structures or configurations could be used and is covered within the scope of the present disclosure.
The intelligent video monitoring system based on deep learning techniques for automatic surveillance, characterized in that includes a surveillance monitoring unit and a center computing unit.
The surveillance monitoring unit comprises a series of visual sensor house memory storage video recorder, a walking area memory storage video recorder, a memory storage video recorder society, corridor memory storage video recorder, switch, decoder, display device wall,

The surveillance monitoring unit is used to detect any viewing video visible within the viewing sight of the monitor and determining whether the detected viewing video belong to authorized individuals who are authorized to view video surveillance footage on the monitor or if any of the detected viewing video belong to individuals who are not authorized to view video surveillance footage on the monitor.
The center computing unit is used as execute programmed instructions stored in the memory for processing a set of sample video frames, using a Deep learning algorithm.
The center computing unit is used to generate a training set, wherein a training set enables detection of one or more person from a set of person, in a geographical area.
Tithe set of sample video frames comprise one or more sample video frames, connected to an alert unit, visual sensor through a wireless communication are respectively connected with the house memory storage video recorder, video visual sensor and other video visual sensor through a wireless communication are respectively connected with walking area memory storage video recorder, wherein other visual sensor video visual sensor are connected through the wireless communication recess memory storage video recorder, other visual sensor video visual sensor are connected through the wireless communication corridor memory storage video recorder, house memory storage video recorder, walking area memory storage video recorder, a memory storage video recorder society, corridor memory storage video recorder, switch center computing unit through the wireless communication and are respectively connected with the switch, the alert unit is connected with the center computing unit through the wireless communication..
The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any block diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.
Although implementations of the invention have been described in a language specific to structural features and/or methods, it is to be understood that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of implementations of the invention.