KR101513215B1 - The analyzing server of CCTV images among behavior patterns of objects - Google Patents

Abstract

The present invention is related to an analyzing server of CCTV images among behavior patterns of objects which includes a main subject part formed with a microprocessor, a CCTV image analysis API, an object recognition module, and a transmitting and receiving part. The CCTV image analysis API receives original image data from a digital observation camera, saves the original image data to long-term saving memory, performs chain cording in the area where resolution change is occurred to a square type object, and transmits the digital zoomed square type object to the object recognition module. The transmitting and receiving part measures transmitting traffics with a center control server of an integrated security control center connected through the network. The main subject part transmits a low capacity-compressed image file, which is obtained by encoding and compressing the original image data received in the object recognition module into a low capacity and low resolution lower than or equal to the traffics measured in the transmitting and receiving part, to the center control server of the integrated security control center. The object recognition module transmits a classified object image classified from square object images and behavior pattern images obtained by extracting behavior patterns from the object images to the main subject part. The main subject part compares a dangerous object and behavior pattern and the square object images stored in the long-term saving memory, applies a priority feature value for dangerous behaviors, and transmits a dangerous event compressed file which is encoded and compressed to the center control server of the integrated security control center.

Description

The object behavior pattern CCTV image analysis server (CCTV images among behavior patterns of objects)

Domestic and foreign recognition technology mainly uses feature point extraction to accurately determine the characteristics of neural networks and patterns.

Existing neural networks require a lot of database acquisition and learning speed due to structural problems.

However, in the existing research, rather than solving such a structural problem, new feature points, new changes, and optimizing factors used in neural networks are mainly studied in order to improve the recognition rate while using the same structure.

In order to improve the recognition rate and the learning speed, it is desirable to study the structural problem of the existing neural network.

For intelligent surveillance systems such as object tracking, facial recognition, and car number recognition, at least two cameras including at least a stationary camera for monitoring a wide area and a PTZ camera for capturing PTZ (Pan Tilt Zoom) of an event area were required.

However, when the event area detected by the high-performance multi-sensor (screaming sound detection through a sound sensor) or the image analysis (behavior recognition, etc.) used as a method for detecting the event area is overlapped, PTZ simultaneous photographing is impossible There has been a problem with the situation.

In addition, studies have shown that if a person monitors CCTV images continuously for 22 minutes, the probability of recognizing a dangerous situation drops to less than 5%.

The present invention is a future crime prediction system using hippocampal neural network learning of object behavior pattern that predicts future crime such as movie 'Minority Report' and provides monitor event function.

(1 ~ 2.3Mega Pixels) PTZ camera (or thermal camera) is not used in combination with a stationary type camera that captures a wide area with a surveillance camera installed in a crime prevention zone or a child protection area, At least 5 Mega Pixels or more and 10 Mega Pixels or more is recommended.) It is characterized by using fixed camera.

That is, the image acquired by the image pickup unit requires a different method and resolution from the conventional shot image, and the requirements according to the above characteristics and the image analysis technique require new technology (novelty) that does not exist in the past.

In the conventional method of extracting and transmitting the event image to the monitor agent in real time to enhance the efficiency of monitoring, the pattern key point extraction of the image object is mainly used by a physical sensor response or a software method. In the present invention, Is a known technology that can be added. To solve the structural problem of the neural network, it is necessary to use a fixed camera with a minimum resolution of 5 mega pixels or more and a high resolution fixed camera The event image of the object behavior pattern is extracted by a software method using the image segmentation variable recognition technique, and the probability of future occurrence crime using the hippocampal neural network learning is predicted and displayed on the event image, It provides the visual event.

An event image of an object motion pattern is extracted by a software method using an image segmentation variable recognition technique, and an event image of a future occurrence crime priority event image using the hippocampal neural network learning is extracted from the event image Provide predictive display to monitor personnel.

In this paper, we propose a method to extract the event image of the object behavior pattern by using the image segmentation variable recognition technology for the image captured by the fixed high resolution cameras of at least 5 megapixels without applying many special cameras to realize special functions. Predictive future crime probability using the hippocampal neural network learning is predicted and displayed on the event image to provide a futuristic crime occurrence probability priority event object image to the monitoring agent to construct a future prediction intelligent crime prevention system different from the conventional prior art.

1 to 4 are schematic views of an embodiment

(Not less than 5 megapixels and at least 10 megapixel megapixel digital camera recommended) without the use of conventional camera technology that combines a fixed camera that captures a wide area and a PTZ camera that enlarges the PTZ zoom. The PTZ function is implemented by software rather than the PTZ mechanical operation as described above, and the probability of occurrence of a future crime is calculated through object classification to provide the PTZ function to the monitoring personnel in advance.

In one embodiment, when the photographed screen is divided into segments and expanded by software and scanned by a number recognition and object recognition program, a crime probability priority value (weight value) is given 100% When the object is a person, it keeps the monitor popup. If the object is a person, it assigns a priority feature value of 50%. Then, when the object such as a knife, a bat, and a mouth is further retrieved, the weight value is 80% Raise the floor weight value to 100% and determine the monitor popup priority according to the weight value. In addition, according to the behaviors such as 'pass', 'watch', and 'report' We apply the hippocampal neural network algorithm to calculate the probability of future crime and learn object behavior pattern CCTV image analysis server.

The high resolution image reconstruction (super resolution) technique transforms a low resolution image into a high resolution image using a signal processing technique (warping, burring, down sampling) between a high resolution image and a low resolution image. Warping uses an interpolation method (bilinear or nearest neighbor) to transform a shape of a part of an image into a shape suitable for a defined area.

In the present invention, in order to secure a wider intermittent area, a focal length of a high-resolution camera lens is shortened to obtain a wide-range omni-directional image, or a super-wide-area or panoramic image is obtained through a plurality of cameras.

In one embodiment, three or six cameras at a wide angle of 60 degrees may be photographed to obtain images of 180 degrees or 360 degrees.

When warping the event objects such as vehicles, people, and license plates in a wide-range image, the object is enlarged by a super resolution method if the object size is small, and is reduced by the high resolution image restoration method when the size of the vehicle is large .

Conventional technology is usually composed of two or more surveillance cameras. It monitors the coordinates of the XY axis where the pixel changes in the image through the omnidirectional surveillance camera that shoots the wide area. While the surveillance camera is zoomed in and photographing the pan / tilt lens, The lens zoom-in is quantified, and the Z-axis coordinate is calculated, and the data is calculated by digitizing it into a three-dimensional image.

Particularly, in order to control illegal parking, it is necessary to calculate the focal distance, pan angle, tilt angle (pan angle, tilt angle, and focal distance) of the surveillance camera, It is possible to reduce the formation and errors of the zone (ZONE).

Since the conventional techniques described above are processed by a mechanical method such as a mechanical pan / tilt lens zooming, accuracy is low due to occurrence of errors, mechanical defects and failures. When there are plural objects (people, vehicles) The number of objects that can be processed simultaneously has been limited.

Also, smoothing, histogram equalization, and expansion or contraction morphology techniques are used as image preprocessing techniques for captured images, and low pass filtering, Gaussian filtering, median filtering, and the like are applied to night images. And the input image is processed according to a histogram algorithm, an edge algorithm, a labeling algorithm and the like without pre-processing for a weekly image. In the histogram algorithm, the lowest bit value frequently generated is assigned And as the frequency is reduced, a larger bit value is assigned to analyze the image value.

The edge algorithm is a method of recognizing the part of the image where the color or density changes rapidly as an edge.

The labeling algorithm extracts the area expected to be an object (person, vehicle) based on the result analyzed above. Through this process, the number, size, color, and coordinates of the person, vehicle, etc. in the event detection area .

In the case of nighttime, it extracts and classifies the event object through the above process. In the nighttime, it uses the infrared ray perspective filter "IR X-RAY VISION CCTV FILTERS" (URL http://www.kaya-optics.com/) , And fog. In addition, it implements various application technologies such as intruder surveillance by applying thermal imaging camera.

In order to binarize the image, the Gaussian distribution is calculated by the average and variance values of the input image and the preprocessed image, and threshold values are determined. Then, a plurality of binarized images are subjected to four-directional (up / a region boundary operation is performed by a chain coding technique and an object rectangle value is generated by an area boundary operation.

When a rectangular object value is created, object classification (people, face, license plate (limitation condition according to the license plate standard, such as numerical height ratio, interval distance matching, etc.) is performed.

For each object data separated after object image correction, the recognition engine recognizes face recognition, key, clothes color, and license plate data by detailed recognition of the object, and the recognition engine uses the hippocampal neural network learning technique.

More specifically, content-based features are extracted using the color texture among the MPEG-7 standardized visual descriptors in the edge algorithm and the labeling algorithm, and the feature data is reduced by applying the PCA technique, In the recognition process, the face recognition, the key, and the face recognition are performed by the recognition engine for each object data that is classified after the object image correction and the license plate (matching operation by the restriction condition according to the license plate standard, that is, the numerical height ratio, The color of clothes, and license plate data to predict the future crime probability of the crime, and provide the event object image to the monitoring agent in the order of probability of occurrence of future crime.

In one embodiment, a behavior pattern is extracted from an image obtained from an ultrahigh-resolution surveillance camera, and the extracted behavior pattern image is compared with a pre-stored behavior pattern image to automatically determine a dangerous situation in the surveillance area. A rank weight value is given.

Here, the motion pattern includes not only an operation of a person or an animal in the acquired image, but also an object such as a dangerous tool or a dangerous impression in the acquired image, and may be an additional image added to the fixed image. In addition, a certain pattern is extracted from the acquired image and is newly registered in the behavior pattern DB. The object data including the dangerous tool and the risk impression capture capture a single frame, and the human behavior pattern extraction captures a plurality of frames can do.

That is, the object data can be confirmed as a single frame capture image, and the human action pattern can be confirmed by extracting a plurality of frames.

According to the embodiment of the present invention, the video surveillance system using the probability of occurrence of the future crime using the hippocampal neural network learning of the object behavior pattern pops up the screen of the CCTV where the dangerous situation occurs, After the monitor is monitored, 'pass', 'watch', and 'report' are determined.

Therefore, according to the present invention, it is possible to overcome the limitation that the surveillant can not monitor a plurality of CCTVs in total according to the CCTV image analysis server and the image processing method using the hippocampal neural network learning of the present invention, It is possible to act actively in anticipation of the occurrence of an emergency such as a crime prevention function and an early suppression of a crime.

An item for each data type of the behavior pattern database may be classified into data types consisting of an abnormal behavior pattern, a risk tool, a risk impression description, an obscured face, etc., and each data type is classified into at least one data item having priority do.

For example, if a person wearing a hat, a knife, or an object act is involved, a woman wearing a hat in the elevator is boarded, a person wearing a hat is caught in the image, The feature value (weight value) is given to the monitor according to priority. If the monitor person judges the situation and recognizes that it is not a dangerous situation, if the "Pass" button / switch is pressed, the CCTV surveillance camera is changed to the original state.

However, if it is judged to be a dangerous situation, enter the option to "watch" and notify the agency promptly with the "report" button if it is necessary to report it.

In one embodiment,

Digital surveillance cameras should be at least 5 megapixels and at least 10 megapixels. Object Action Pattern The CCTV image analysis server receives the original image data photographed by the digital surveillance camera, stores the original image data in a long term storage memory of the main controller, and stores the original image data in the object recognition module hippocampal neural network memory.

Currently, the bandwidth of the leased line can accommodate 10Mbps and 100Mbps, but in case of 100Mbps, the communication cost is excessive, so the original image data is stored in the long-term storage memory, and if necessary, I will.

Object Behavior Pattern The CCTV image analysis server transmits the low-capacity image compression file that encodes the original image data received in the hippocampal neural network memory to low-quality and low-capacity low-capacity image compression (H.246, MPEG, etc.) through the network to the integrated security center center control server .

The local server divides the original image data received in the hippocampal neural network memory into one or more divided images on the X-axis and Y-axis so as to store and store the X-Y coordinate values and the time data.

The object behavior pattern CCTV image analysis server digitally zooms the stored divided images.

Referring to FIG. 1, after the original image data is divided into 10 parts in the X axis and 10 parts in the Y direction, it is possible to increase the enlargement ratio of the distant divided images and to reduce the enlargement ratio of the divided images at the near distances.

In addition, as shown in FIG. 3, the distance can be enlarged by making the divided screen small, and the divided screen can be enlarged and the enlargement can be made small.

Object behavior pattern The CCTV image analysis server digitally enlarges the stored images and then extracts the behavior patterns and compares them with the risk behaviors stored in the long term storage memory, To the integrated security center control server.

Object Action Pattern The CCTV image analysis server sets at least one of the illegal traffic regulation area, the traffic lane area, the over speed restriction area, and the signal violation restriction area among the divided images of the image segmentation step.

Object behavior pattern The CCTV image analysis server classifies each digital zoom enlarged divided image into numbered recognition objects by number recognition program and classified objects after object recognition by scanning object recognition program.

Object Behavior Pattern When CCTV image analysis server recognizes the vehicle number of the license plate after the number recognition scan at the object recognition phase, it compares the vehicle number of the vehicle license plate with the vehicle license plate number. The compressed file is transferred to the integrated security center control server.

Object behavior pattern When the CCTV image analysis server recognizes over illegal parking time in illegal parking area, it sends illegal parking data to the integrated center of crime prevention center control server Lt; / RTI >

Object behavior pattern CCTV image analysis server judges the vehicle license plate number to be recognized when the vehicle object is recognized illegally in the illegitimate intermittent area during the illegal parking event transmission step and the vehicle object is continuously recognized but the license plate number is not recognized. It is stored in the hippocampal neural network (hippocampal neural network memory) for the vehicle object and tracks the event over illegal parking time.

Object Behavior Pattern CCTV image analysis server detects the event vehicle object and the obscured vehicle license plate at the tracking phase when they are detected in the illegal parking intermittent area continuously for more than illegal intermittent intermittent time period and the preference weight value of feature value The license plate number is not recognized due to parking, and the number plate is not recognizable due to the diagonal parking), the vehicle license plate of the vehicle object is judged as the car license plate of the deliberately obscured vehicle object, Or if the numbered license plate is recognized again, the illegally parked vehicle event compression file encrypted and compressed with the image of the recognized point of time and license plate information is transmitted to the integrated security center control server.

Object Behavior Pattern CCTV image analysis server encrypts the vehicle image and license plate information when the license plate is recognized in the lane-lane area of the event area in the event division image setting phase. To the center center control server.

Object behavior pattern The CCTV image analysis server is a system that detects the object detected by the laser detector that focuses on the loop coil or overspeed area installed in the overspeed interception area is the vehicle and the speed is over speed interception target, And transmits the overspeed intermittent vehicle event compression file encrypted and compressed to the integrated security center control server.

Object behavior pattern The CCTV image analysis server encodes and compresses the image of the vehicle and the license plate information if the object detected by the laser detector which focuses on the loop coil or the overspeed interception area in the overspeed interception area is the vehicle and the interception of the signal violation Signal violation vehicle compression file to the integrated security center control server.

The centralized control server of the integrated crime prevention center is composed of at least one received risk event compression file, low capacity image compression file, wastebaservice event compression file, illegal parking event compression file, and obscured event compression file received through the network from the object behavior pattern CCTV image analysis server In the event that one or more of the illegal parking vehicle event archive file, the violating vehicle event archive file, the overspeed intermittent vehicle event archive file, and the signal infringement vehicle archive file, the center control server stores the illegal parking event compression file, File, a vehicle event compression file and a overspeed intermittent vehicle event compression file on the side of a vehicle, the system transmits a negative charge to the intermittent server to issue a fine.

The center control server decompresses the low-capacity image compressed file and displays it on the monitor as a divided screen. However, the center control server compares the priority values assigned to the dangerous behavior priority characteristic value and the demand event compression file, The object behavior pattern is a CCTV image analysis server.

In one embodiment,

It consists of a main processor consisting of a microprocessor with O / S, an application programming interface (API), an object recognition module including a hippocampal neural network memory, and an object behavior pattern CCTV image analysis server. The description of the basic configuration such as the input / output device (mouse, keyboard, USB port, monitor, etc.) is omitted for the sake of clarity of the present invention.

The image analysis API transmits the original image data captured by the ultra-high resolution digital surveillance camera of 5 megapixel or more and stores the original image data in the long-term storage memory and transmits it to the hippocampal neural network memory.

The image analysis API recognizes the pixel change boundary in the original image data as edge in the edge algorithm and the area in which the color or density is rapidly changed, extracts the object region by the labeling algorithm, boundary operation is performed to form an object rectangle by an area boundary operation, the image is enlarged / reduced according to the size of the object by the super resolution method (if the object size is large, the object rectangle is enlarged, The rectangular object image is transmitted to the object recognition module.

The transceiver measures the transmission traffic to the integrated security center control server connected through the network and transmits the measured value to the main controller.

The main control unit transmits a low-capacity image compression file, which is obtained by encrypting and compressing the original image data received in the hippocampal neural network memory to a low-quality and low-capacity low-quality image equal to or lower than the traffic measured by the transceiver unit, to the integrated security center control server through the network.

For example, if 5M (Mega Pixels) original image data received in a hippocampal neural network memory photographed by a 5M (Mega Pixels) digital surveillance camera is 3Mbps (Mega bits per second) measured by the transceiver, (Mega Pixels), which is a low-resolution, low-capacity pixel of less than 50% of the measured traffic (for example, 50% of the measured traffic), or a low-quality image obtained by reducing the number of transmission frames by 1/3. Through the integrated security center control center server.

The object recognition module transmits the object classification image classified by recognizing the face recognition, the key color, the clothes color, the license plate, and the weapon by the detailed recognition of the object by the hippocampal neural network learning recognition engine after the object classification in the rectangular object image .

The object recognition module extracts a behavior pattern of an object image and transmits a behavior pattern image to a main controller.

The main control unit compares the danger object and the behavior pattern stored in the long term storage memory with the rectangular object image, assigns the risk action priority characteristic value, and transmits the encrypted event compressed file to the integrated security center control server.

For example, when a person is recognized by classifying objects in the rectangular object image, 50% of the dangerous action priority value is applied, and when the face is not recognized due to hat, mask, etc., If the object is recognized, it is added 10%. 70% is applied. After extracting the behavior pattern of the object image, if the object moves fast, it is applied 10% or 80% If a part of the body moves fast, it adds 10% to 80%. If the body of the opponent falls, it adds 20% to give a 100% risk behavior priority feature value.

As mentioned above, 50% ~ 100% of the risk behavior priority value pops up on the monitor according to the priority value, so that future crime occurrence probability priority event popup is concentrated on the image-based monitoring with high probability of crime to predict and prevent crime. do.

For example, when a captured image is divided into software, the image is scanned by a number recognition and object recognition program, and when a vehicle of a wander vehicle is searched, a crime probability priority value (weight value) is 100% If the object is a person, it gives a priority feature value of 50%. Then, when the object such as a knife, bat, or wand is additionally retrieved, the weight value is 80%. If the movement speed of the object or the movement of the body part becomes faster We increase the weight value to 100%, determine the priority of the monitor popup according to the weight value, and learn the feature vectors at high speed according to the behaviors such as 'pass', 'watch' We apply the neural network algorithm to calculate the probability of future crime and learn object behavior pattern CCTV image analysis server.

The main control unit compares the vehicle license plate object classification image with the number recognition program and then compares the vehicle license plate number with the vehicle number of the picked-up vehicle. And transmits the compressed file to the centralized security center control server.

In one embodiment,

It is related to CCTV image analysis server, which is composed of microprocessor, object analysis API, object recognition module, and transceiver.

The image analysis API receives original image data photographed by a digital surveillance camera, stores original image data in a long-term storage memory, chained an area where a pixel change occurs in the original image data to a rectangular object, And transmits the image to the object recognition module.

The transceiver measures transmission traffic with the integrated security center control server connected through a network and transmits the measured traffic to the main controller.

The main control unit transmits a low-capacity image compression file, which is obtained by encrypting the original image data received by the object recognition module to a low-quality low-capacity image equal to or lower than the traffic measured by the transceiving unit, to the integrated security center control server through the network.

The object recognition module transmits a behavior pattern image obtained by extracting an object classification image and a behavior pattern of the object image classified by the object in the rectangular object image to the main controller.

The main control unit compares the danger object and the behavior pattern stored in the long term storage memory with the rectangular object image to give a dangerous action priority characteristic value and transmits the risk event compression file encrypted and compressed to the centralized security center control server Is an object behavior pattern CCTV image analysis server.

In one embodiment,

An object behavior pattern CCTV image analysis server configured with a main processor unit composed of a microprocessor, an image analysis API, an object recognition module, and a transceiver unit, wherein the image analysis API receives original image data shot by a digital surveillance camera, And storing the original image data in a storage and object recognition module hippocampal neural network memory; Wherein the transceiver measures transmission traffic to a centralized security center control server connected through a network and transmits the measured traffic to a main controller; The main control unit transmits a low-capacity image compression file, which is obtained by encrypting the original image data received by the object recognition module to a low-quality and low-capacity image data of less than the traffic measured by the transceiving unit, to the integrated security center control server through the network. Wherein the object recognition module divides the original image data received in the hippocampal neural network memory into at least one X axis and Y axis to superimpose the XY coordinate value and the time data, Wherein the object recognition module transmits an object classification image obtained by classifying the object recognized after recognizing the object by the object recognition program to each of the digital zoom enlarged divided images and the divided images to the main language unit; The main control unit compares the behavior pattern image classified into the object classification image and the divided image with the risk object and the behavior pattern stored in the long term storage memory to give a dangerous behavior priority characteristic value, And transmits the event compression file to the integrated security center control server.

In the above,

Comparing the object classification image recognized by the object recognition module with the dangerous object stored in the long term storage memory of the subject section, and assigning a risk action priority characteristic value according to a person, a hatted face, an obscured face, a wanted person, For the behavior pattern image classifying the behavior pattern of the divided images, the main subject section compares the behavior pattern of the movement of the body part, which is stored in the long term storage memory, the behavior pattern of the human object, And a risk behavior priority characteristic value is assigned to the CCTV image analysis server.

In the above,

When the vehicle license plate is recognized in the object classification image, the main control unit compares the vehicle license plate text of the vehicle number plate with the license plate text by the number recognition program, assigns the priority characteristic value to encrypt the object classification image, And the risk event compression file is transferred to the integrated security center control server.

In the above,

If the vehicle license plate is recognized in the illegal parking area of the object classification image, the data of the vehicle license plate is textized by the number recognition program when the parking lot is illegally parked. File to a centralized security center control server.

In the above,

The main control unit transmits an illegal parking event data compression file encrypted with the vehicle object image and the license plate information to the integrated security center control server when the illegal parking vehicle object is recognized for the intermittent time or longer in the illegal parking intermittent area; The main control part judges that the vehicle object is the license plate number of the first vehicle in the illegal parking area and then recognizes the vehicle object but recognizes the vehicle license plate if the license plate number is not recognized and stores the car object in the hippocampal neural network memory Event tracked and tracked event over illegal parking time If the vehicle object and the license plate are continuously detected illegally in the illegal parking area for more than the illegal parking time, it is judged to be the car license plate of the car object that is deliberately obstructed. If the current image or the obscured number plate is recognized again from the recognized image immediately before the judgment, the illegally parked vehicle event compression file encrypted and compressed at the recognized point of view and the license plate information is transmitted to the integrated security center control server Featured Body action patterns CCTV the video analysis server.

In the above,

The integrated security center center control server decompresses the low-capacity image compression file transmitted from the main body of the object behavior pattern CCTV image analysis server and displays it as a divided screen on the monitor but preferentially pops up the monitor according to the priority of the priority characteristic value And displays the object behavior pattern CCTV image analysis server.

In one embodiment,

Object behavior pattern The CCTV image analysis server receives the original image data taken from the digital surveillance camera and stores the original image data in the long term storage memory and transmits it to the hippocampal neural network memory.

Object Behavior Pattern The CCTV image analysis server sends the low-capacity image compression file, which is encrypted and compressed with low-quality low-capacity original image data received in the hippocampal neural network memory, to the integrated security center center control server through the network.

The local server divides the original image data received in the hippocampal neural network memory into one or more divided images on the X-axis and Y-axis so as to store and store the X-Y coordinate values and the time data.

Object Behavior Pattern The CCTV image analysis server sets illegitimate intermittent intermittent regions among the divided images of the image segmentation step.

Object behavior pattern The CCTV image analysis server digitally enlarges the stored divided images.

Object behavior pattern The CCTV image analysis server classifies each digital zoom enlarged divided image by number recognition program, number recognition scan, and object recognition program to classify recognized objects after object recognition.

Object Behavior Pattern When CCTV image analysis server recognizes the vehicle number of the license plate after the number recognition scan at the object recognition phase, it compares the vehicle number of the vehicle license plate with the vehicle license plate number. The compressed file is transferred to the integrated security center control server.

Object behavior pattern When the CCTV image analysis server recognizes over illegal parking time in illegal parking area, it sends illegal parking data to the integrated center of crime prevention center control server Lt; / RTI >

Object behavior pattern CCTV image analysis server judges the vehicle license plate number to be recognized in vehicle illegally intermittent intermittent area and recognizes vehicle object in case illegal intermittent event transmission step, To the hippocampal neural network (memory of the hippocampal neural network) and track the events over illegal parking time.

Object Behavioral Pattern The CCTV image analysis server stores the event vehicle object in the tracking phase and the obscured vehicle license plate in the illegal parking intermittent area continuously for more than the illegal parking intermittent time period and store it in the long term memory or short term memory according to the preference weight value of the characteristic value and deliberately If the current image or the hidden license plate is recognized again from the recognized image, the image and the license plate information of the recognized point of view are encrypted and compressed. The parking vehicle event archive file is transferred to the integrated security center control server.

The center control server of the integrated crime prevention center can control at least one of the low-capacity image compression file received from the object behavior pattern CCTV image analysis server through the network, the archive file event archive file, the illegal divergence event archive file, and the obscured illegal parking vehicle event archive file As a result,

The center control server transmits at least one illegal parking event archive file, the illegally parked vehicle event archive file to the intermittent server, decompresses the low capacity image archive file and displays it in a split screen on the monitor. And displays the object behavior pattern CCTV image analysis server.

In one embodiment,

Object Action Pattern The CCTV image analysis server receives original image data taken from a digital surveillance camera, stores the original image data in a long-term storage memory, and transmits the original image data to the hippocampal neural network memory. Wherein the object behavior pattern CCTV image analysis server comprises: a video transmission step of transmitting a low-capacity image compression file, which is obtained by encrypting and compressing original image data received in the hippocampal neural network memory to a low quality and low capacity, to a centralized control center control server through a network; The local server splits the original image data received in the hippocampal neural network memory into one or more divided images in the X axis and the Y axis so as to match and store the XY coordinate values and the time data; Object behavior pattern The CCTV image analysis server digitally zooms the stored divided images; Object Action Pattern The CCTV image analysis server classifies each of the digital zoom enlarged divided images into number recognition programs by number recognition programs and object recognition programs by classifying objects recognized by scanning and object recognition programs. Object Behavior Pattern When CCTV image analysis server recognizes the vehicle number of the license plate after the number recognition scan at the object recognition phase, it compares the vehicle number of the vehicle license plate with the vehicle license plate number. Transmitting a compressed file to a centralized security center control server; The center control server of the integrated crime prevention center decompresses the low-capacity image compression file in the at least one low-capacity image compression file and the screening vehicle event compression file received from the object behavior pattern CCTV image analysis server through the network The object behavior pattern CCTV image analysis server is characterized in that it is displayed as a split screen on a monitor, but is preferentially popped up upon receipt of a screen event file.

In one embodiment,

Object Action Pattern The CCTV image analysis server receives original image data taken from a digital surveillance camera, stores the original image data in a long-term storage memory, and transmits the original image data to the hippocampal neural network memory. Wherein the object behavior pattern CCTV image analysis server comprises: a video transmission step of transmitting a low-capacity image compression file, which is obtained by encrypting and compressing original image data received in the hippocampal neural network memory to a low quality and low capacity, to a centralized control center control server through a network; Dividing the original image data received in the hippocampal neural network memory into one or more divided images in the X axis and Y axis and storing the XY coordinate values and the time data in a matching manner; The object behavior pattern CCTV image analysis server digitally enlarges a stored divided image and extracts a behavior pattern and compares the risk behavior stored in the long term storage memory with a dangerous action priority characteristic value, A risk event image transmission step of transmitting the file to the centralized security center control server; The center control server decompresses the low-capacity image compression file received from the object behavior pattern CCTV image analysis server and displays it as a divided screen on the monitor. However, the center control server compares the risk behavior priority characteristic values given to the low- The object behavior pattern is a CCTV image analysis server using hippocampal neural network learning.

In one embodiment,

In the above description, the XY coordinate value and the time data are matched with each other by dividing the XY coordinate value into one or more divided images on the X axis and the Y axis, so that the size of the divided image is relatively decreased from near to far in the Z axis of the original image, And the XY coordinate value and the time data are matched by dividing the X-axis Y-axis into one or more divided images and replacing the XY coordinate value with the time data.

In one embodiment,

In the above, the priority characteristic values are compared and popped up and displayed according to priority, and the object behavior pattern CCTV image analysis server is stored in the long term storage memory according to the 'pass', 'watch', and 'report' The object behavior pattern is a CCTV image analysis server using the hippocampal neural network learning of the object behavior pattern.

In one embodiment,

The digital surveillance camera is an object using the hippocampal neural network learning of the object behavior pattern, which is replaced with one of a 200-mega pixel digital surveillance camera, a 270-pixel small surveillance camera, a 500-mega pixel digital surveillance camera, Behavior pattern is a CCTV image analysis server.

1 ~ 2: Split image

Claims (9)

delete An object behavior pattern CCTV image analysis server comprising a main processor unit comprising a microprocessor, an image analysis API, an object recognition module, and a transmission / reception unit,
The image analysis API receives original image data photographed by a digital surveillance camera, stores original image data in a long-term storage memory, chained an area where a pixel change occurs in the original image data to a rectangular object, Transmits the image to the object recognition module;
Wherein the transceiver measures transmission traffic to a centralized security center control server connected through a network and transmits the measured traffic to a main controller;
The main control unit transmits a low-capacity image compression file, which is obtained by encrypting the original image data received by the object recognition module to a low-quality and low-capacity image data of less than the traffic measured by the transceiving unit, to the integrated security center control server through the network.
Wherein the object recognition module transmits a behavior pattern image obtained by extracting a behavior classification pattern of the object classification image and an object image classified by the object in the rectangular object image to the main language unit;
The main control unit compares the dangerous object and the behavior pattern stored in the long term storage memory with the object classification image and the behavior pattern image of the rectangular object image to give a dangerous action priority characteristic value, And transmits the object behavior pattern to the center control server.

delete 3. The method of claim 2,
Comparing the object classification image recognized by the object recognition module with the dangerous object stored in the long term storage memory of the subject section, and assigning a risk action priority characteristic value according to a person, a hatted face, an obscured face, a wanted person,
For the behavior pattern image classifying the behavior pattern of the divided images, the main subject section compares the behavior pattern of the movement of the body part, which is stored in the long term storage memory, the behavior pattern of the human object, And assigning a risk action priority characteristic value to the CCTV image analysis server.

3. The method of claim 2,
When the vehicle license plate is recognized in the object classification image, the main control unit compares the vehicle license plate text of the vehicle number plate with the license plate text by the number recognition program, assigns the priority characteristic value to encrypt the object classification image, And the risk event compression file is transmitted to the integrated security center control server.

3. The method of claim 2,
If the vehicle license plate is recognized in the illegal parking interception area in the object classification image, the subject control section encrypts the data of the vehicle license plate by the number recognition program and the object classification image. To a centralized security center control server.

delete 3. The method of claim 2,
The integrated security center center control server decompresses the low-capacity image compression file transmitted from the main body of the object behavior pattern CCTV image analysis server and displays it as a divided screen on the monitor but preferentially pops up the monitor according to the priority of the priority characteristic value And displaying the object behavior pattern of the CCTV image analysis server.
3. The method of claim 2,
A digital surveillance camera is an object behavior pattern CCTV image analysis server that is replaced by one of a 200-mega-pixel digital surveillance camera, a 270-mega-pixel digital surveillance camera, a 500-megapixel digital surveillance camera, and a 1000-megapixel digital surveillance camera.

Images