CN101765025A - System for abnormal detection of surveillance camera and method thereof - Google Patents
System for abnormal detection of surveillance camera and method thereof Download PDFInfo
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Abstract
The invention relates to a video monitoring system and a method thereof, in particular to a system for an abnormal detection of a surveillance camera and a method thereof. The invention detects whether the surveillance camera is abnormal with a moving target detecting method and/or a stationary target detecting method and sends alarm information to the user terminal when the abnormality of the surveillance camera is detected. If the ratio of the moving target taking up the entire video image is greater than or equal to a threshold value of abnormal moving target area, and the condition duration is longer than or equal to a threshold value of abnormal moving target time, the surveillance camera has an abnormality caused by the moving target. If the ratio of the stationary target taking up the entire video image is greater than or equal to a threshold value of abnormal stationary target area, and the condition duration is longer than or equal to a threshold value of abnormal stationary target time, the surveillance camera has an abnormality caused by the stationary target. The method for the abnormal detection of the surveillance camera is more intelligent and has more important practical significance than the manual detection of the surveillance camera.
Description
Technical field
The present invention relates to video monitoring system and method, relate in particular to a kind of system and method that detects based on the monitoring camera unit exception of moving object detection and static target detection.
Background technology
Monitored video camera system is widely used in each monitoring field, as intelligent transportation system etc.In present video monitoring system, the position of most monitoring camera equipment all is pre-fixed, and remains constant in monitor procedure.Yet monitoring camera equipment in use, usually can suffer the destruction of extraneous factor, as artificial damage, block or move etc.; Video data in the monitoring camera equipment also may be polluted simultaneously, much noise occurs such as the video data that monitors, and violent shake etc. takes place video pictures.Monitoring camera equipment is destroyed or monitor data is contaminated, all can influence monitoring camera equipment operate as normal.After monitoring camera equipment takes place unusually, need in time adjust, repair or change to monitoring camera equipment.Therefore, whether normal, and announce the result to the user particularly important if must detect monitoring camera equipment at any time.
In traditional video monitoring, check whether video monitoring equipment unusual, the method for employing normally, at user side by the manual observation camera, and according to observing content judge whether picture pick-up device takes place unusually.Obviously, artificially detect monitoring camera equipment and whether have obvious defects and deficiency unusually, observe camera incessantly, can cause observing personnel's vision and physical fatigue.
In video monitoring system, need detect monitoring camera equipment in real time and whether take place unusually, so that in time to unusual place under repair of monitoring camera equipment or replacing occurring.In view of there is inevitable drawback in the manual detection picture pick-up device, a kind of monitoring camera abnormality detection system and method for intelligence have very important realistic meaning and application prospects.
Summary of the invention
The invention provides the system and method that a kind of monitoring camera unit exception that can overcome the above problems detects, purpose is to replace manually and then intelligently monitoring camera equipment is carried out abnormality detection.
In first aspect, the invention provides a kind of monitoring camera unit exception detection system, this system comprises that acquisition module, abnormality detection module, alarm module, parameter are provided with module, memory and processor.
Acquisition module obtains the video image of monitoring camera equipment monitor scene; Whether the abnormality detection module detects monitoring camera equipment by video image information and takes place unusually; Alarm module sends warning message to user terminal based on the abnormal conditions that take place; Parameter is provided with module and system parameters is set so that whether judge monitoring camera equipment takes place unusually, and system parameters comprises prospect threshold value, abnormal motion target area threshold value, abnormal motion object time threshold value, unusual static target area threshold, unusual static target time threshold.
The abnormality detection module comprises motion analysis module and/or static target detection module; State institute's duration that the motion analysis module accounts for the ratio of whole video image and has this ratio according to moving target, judge monitoring camera equipment whether take place by moving target cause unusual; State institute's duration that the static target detection module accounts for the ratio of whole video image and has this ratio according to static target, judge monitoring camera equipment whether take place by static target cause unusual.
In second aspect, the invention provides a kind of monitoring camera unit exception detection method, detect video monitoring equipment by moving object detection method and/or static target detection method and whether take place unusually; If this monitoring camera equipment takes place unusual, warning message is sent to user terminal, terminal is reported to the police.
The moving object detection method is according to ratio and this state institute's duration that moving target accounts for the whole video image, judge monitoring camera equipment whether take place by moving target cause unusual; The static target detection method is according to ratio and this state institute's duration that static target accounts for the whole video image, judge monitoring camera equipment whether take place by static target cause unusual.
In one embodiment of the invention, if moving target accounts for the abnormal motion target area threshold value of the ratio of whole video image more than or equal to default, and the time that this state continued is more than or equal to the abnormal motion object time threshold value of default, then this video monitoring equipment be by moving target cause unusual.
In another embodiment of the present invention, if static target accounts for the unusual static target area threshold of the ratio of whole video image more than or equal to default, and the time that this state continued is more than or equal to the unusual static target time threshold of default, then this video monitoring system be by static target cause unusual.
The present invention is by analyzing state institute's duration that moving target accounts for the ratio of whole video image and has this ratio, and/or analyze state institute's duration that static target accounts for the ratio of whole video image and has this ratio, and then judge whether this monitoring camera equipment takes place unusually.System and method of the present invention has overcome the deficiency of traditional detection monitoring camera equipment, and under artificial situation, intelligent and real-time carries out abnormality detection to monitoring camera equipment.
Description of drawings
Below with reference to accompanying drawings specific embodiments of the present invention is described in detail, in the accompanying drawings:
Fig. 1 monitoring camera device hardware of the present invention system block diagram;
Fig. 2 monitoring camera unit exception of the present invention testing process figure;
Fig. 3 flow chart based on moving object detection of the present invention;
Fig. 4 flow chart that detects based on static target of the present invention.
Embodiment
In order to make purpose of the present invention and technical scheme more clear, monitoring camera unit exception detection system of the present invention and method are elaborated below in conjunction with accompanying drawing and embodiment.
Fig. 1 is a monitoring camera device hardware system block diagram of the present invention.This monitoring camera equipment comprises that acquisition module 110, abnormality detection module 120, alarm module 130, parameter are provided with module 140, memory 150 and processor 160.Wherein, abnormality detection module 120 comprises motion analysis module 121, static target detection module 122 and statistical module 123.
Acquisition module 110 is used for the acquisition monitoring picture pick-up device video image of scene on every side.
Whether parameter is provided with the system parameters that module 140 is provided with monitoring camera equipment, take place unusually so that judge monitoring camera equipment.System parameters can comprise prospect threshold value, abnormal motion target area threshold value, abnormal motion object time threshold value, unusual static target area threshold, unusual static target time threshold, and the numerical value of concrete threshold value can be provided with by the user.
Program in the memory 150 storage monitoring camera equipment, and store background image, current frame image, warning message etc.
Program in processor 160 execute stores 150, counting and logical operation in the monitoring camera equipment carried out in the operation of each module of control monitoring camera equipment.
Fig. 2 is monitoring camera unit exception testing process figure of the present invention.Step 210 acquisition monitoring picture pick-up device monitors the video image of scene.Step 220 is carried out abnormality detection according to the video image that collects to monitoring camera equipment, and abnormality detection comprises moving target abnormality detection and static target abnormality detection.Step 230 judges whether monitoring camera equipment is unusual, takes place unusually if detect this monitoring camera equipment, and execution in step 232 sends warning messages to user terminal, and user terminal is reported to the police.The current frame image that execution in step 231 promptly collects in user terminal displays does not take place unusually if detect this monitoring camera equipment; And then it is unusual up to detecting this monitoring camera equipment generation to carry out this abnormality detection flow process incessantly.
Unusual reason takes place with monitoring camera equipment and is divided into two kinds in the present invention, first kind normally monitoring camera equipment violent shake takes place, or monitoring camera equipment is blocked by external object in certain period, or much noise etc. appears in video data.A large amount of moving targets appears in the video image that this kind situation will cause monitoring.Second kind normally picture pick-up device is destroyed, or is moved, or by the spraying dirty material or blocked for a long time etc.A large amount of static targets appears in the video image that this kind situation will cause monitoring.
Whether take place unusually in order to detect monitoring camera equipment, the video image that collects is carried out moving object detection to monitoring camera equipment and static target detects.If when finding to exist a large amount of moving targets or static target even being covered with the whole video picture; illustrate monitoring camera equipment take place by cause unusual of moving target or by static target cause unusual; send warning message to user terminal, so that user terminal is handled monitoring camera equipment.
Whether will set forth moving object detection method of the present invention and static target detection method detection monitoring camera equipment below respectively takes place unusually.
Fig. 3 is the flow chart based on moving object detection of the present invention.Step 310 acquisition monitoring picture pick-up device monitors the video image of scene.Whether step 320 judges system's background modeling by the flag bit that is stored in the storage matrix in the memory 150.If system does not have background modeling, execution in step 321 is a background modeling, and execution in step 322 background images upgrade again.If system is background modeling, directly execution in step 322 background images upgrade, and then the background image after obtaining upgrading.Background image upgrades the back to be continued execution in step 330 and obtains moving target by foreground detection, and promptly current frame image subtracting background image and the binaryzation by collecting carried out the connected domain analysis to bianry image again, and then obtains moving target.Execution in step 340 is promptly calculated the moving target area and is write down this moving target time of occurrence again.
Continue execution in step 350 and judge promptly whether the moving target area reaches abnormal motion target area threshold value, and this moving target area reaches abnormal motion target area threshold value institute's duration and whether reaches abnormal motion object time threshold value.If this moving target area reaches abnormal motion target area threshold value, and this state duration reaches abnormal motion object time threshold value, then illustrate this monitoring camera equipment take place by moving target cause unusual, execution in step 360 sends warning messages to user terminal, the user terminal warning.If the moving target area does not reach abnormal motion target area threshold value, perhaps this moving target area reaches abnormal motion target area threshold value but this state duration does not reach abnormal motion object time threshold value, then illustrate this monitoring camera equipment do not take place by moving target cause unusual; And then carry out incessantly this moving target abnormality detection flow process up to detect this monitoring camera equipment take place by moving target cause unusual.
Next, this moving target method for detecting abnormality is described in detail.
The method of step 321 background modeling is a lot, as averaging method, gauss hybrid models method and Density Estimator method or the like.The factor that long-term rest image gray value constantly changes is a lot, usually As time goes on the gray value of rest image can change gradually, such as each different time stage solar radiation difference of morning, noon, afternoon, therefore the gray value of same object is also different.The purpose that step 322 background image upgrades is the gray value of detected background image of upgrading in time, and it is according to different update policy update background image that background image upgrades the method that adopts, and the update strategy that adopts is a moving average method usually.Scene to the monitoring camera equipment monitor is carried out background modeling and renewal, and purpose is to set up and safeguard the gray value that is in long-term rest image in the monitoring scene.Background modeling is the initialization background image that obtains monitoring scene according to certain idea about modeling, the background image correspondence that background modeling obtains be to monitor the part that immobilizes in the scene.Context update is the image according to current scene, utilizes certain update method to come the initialization background image is constantly revised, enable with current scene in the gray value of background parts be consistent.
After background image upgraded and finishes, execution in step 330 was promptly carried out foreground detection to the current frame image that collects, and the purpose of foreground detection is the moving target that obtains in the video image.The method that foreground detection adopts is with current frame image subtracting background image and binaryzation, then connected component in the moving region to be extracted, and then obtain moving target.Current frame image is the image that is monitored current scene by the monitoring camera that acquisition module 110 collects; Background image is to be stored in the memory 150 and the background image after upgrading; Binaryzation is that the greyscale image transitions with detected current frame image subtracting background image becomes bianry image.
Current frame image subtracting background image and binaryzation, be to compare binaryzation again with background image with current frame image, specifically be that gray value with corresponding position pixel in the gray value of each pixel in the current frame image and the background image subtracts each other, if the result who subtracts each other shows this pixel in bianry image more than or equal to the prospect threshold value of default with white; In like manner, if the result who subtracts each other shows this pixel in bianry image less than the prospect threshold value of default with black.The bianry image of all white and black component movement target and stationary object, white portion is the moving region in this bianry image, black part is divided into stagnant zone.Connected component in the described extraction moving region be with the white in the bianry image not connected component be communicated with, and then obtain the ratio that moving target in the current video image and this moving target occupy the whole video image.
There is certain height in monitoring camera equipment apart from ground under the normal condition, the video image that monitoring camera equipment absorbs is limited in scope, therefore it is less that the moving object in the image occupies the ratio of whole video image, i.e. the white portion area of moving object in bianry image is less with respect to the area of whole monitor video image.Therefore, if moving target time that whole video picture and this phenomenon exist occurs even is covered with when longer in a large number, it is unusual to illustrate that this monitoring camera equipment takes place, and this be unusually by moving target cause unusual, promptly monitoring camera equipment has taken place by shake or other motion phenomenon caused unusual.
In based on the moving object detection method, need parameter that module 140 is set and set abnormal motion target area threshold value and abnormal motion object time threshold value.Abnormal motion target area threshold value at be the ratio that the white portion area accounts for whole bianry image area in the bianry image of moving target, promptly at be the ratio that moving target accounts for the whole video image.Abnormal motion object time threshold value at be that the white portion area accounts for whole bianry image area ratio and reaches moving target area threshold institute's duration in the moving target bianry image, promptly at be that moving target reaches the time that abnormal motion target area threshold value is continued.
The monitoring camera equipment that causes by static target take place unusual after, bigger changeization can take place in background image, influences normal video monitoring.Fig. 4 is the flow chart that detects based on static target of the present invention.
Next, the static target detection method is described in detail.
The foregoing background modeling method of step 421 background modeling method such as this specification based on the moving object detection method.The image that background modeling obtains, promptly background image is to be in long-term static image in the image that monitors, as fixing lawn, stone, trees etc.
The gray value of 123 pairs of detected each pixels of step 430 statistical module is done statistics, just statistical pixel is along with the time changes the situation that its gray value changes, obtain the time dependent gray value frequency of occurrences of certain pixel, and all other pixels in this image that collects are done statistics with this method.
For example in certain period, the monitoring camera monitoring of tools to a certain video image in the gray value of a certain pixel be 60, the gray value of another this pixel of moment is 60 ... the gray value of continuous some these pixels of the moment all is 60, the gray value of another this pixel of the moment is 180, also the gray value of this pixel of the moment is 60, the another plurality of continuous gray value of this pixel constantly is 60, then extract the gray value 60 of this pixel, extract the highest gray value of all pixel frequencies of occurrences of video image with quadrat method.Need to prove that this rest image that obtains is the rest image in the timing statistics, be docked in the interior vehicle of this guarded region etc. in for example long-term static lawn, stone, trees and the timing statistics.
Elaborate in bianry image the method that static target and nonstatic object representation are come out below.
Because all images all are images static in the statistic processes in the rest image, all images also all are static images in the background image, so the result that both subtract each other also is static, do not have moving object.Illustrate, if the rest image that monitors in the timing statistics is lawn, stone, trees and the stationary vehicle that is docked in guarded region, background image is lawn, stone, trees, both subtract each other what obtain is the stationary vehicle that is docked in guarded region in the timing statistics, and then static target is this parked vehicle.The concrete method that adopts is, with the gray value of each pixel in the rest image that counts on, the grey scale pixel value of this position in the subtracting background image.If the result who subtracts each other, illustrates then that this pixel belongs to the pixel as long-term rest images such as lawns, shows in bianry image with black less than the prospect threshold value of default; If the result who subtracts each other is more than or equal to the prospect threshold value of default, illustrate that then this pixel does not belong to the pixel as long-term rest images such as lawns, and belong to rest image pixel as occurring in the statistic processess such as parked vehicle, show in bianry image with white.Therefore, in this bianry image, white portion is represented the static target that counts on, and black part divides expression long-term static object.
In based on the static target detection method, need parameter that unusual static target area threshold and unusual static target time threshold that module 140 is set the static target detection method are set.Unusual static target area threshold at be the ratio that the white portion area accounts for whole bianry image area in the bianry image of static target, promptly at be the ratio that static target accounts for the whole video image.Unusual static target time threshold at be that the white portion area accounts for whole bianry image area ratio and reaches static target area threshold institute's duration in the static target bianry image, promptly at be that static target reaches the time that the static target area threshold is continued.
In the present invention, the context update time need be arranged to greater than static target detection time, in case because context update finish after static target will become background image, therefore static target can't obtain static target after detecting, also just can't according to the area of static target judge monitoring camera equipment whether take place by static target cause unusually.Therefore, after detecting through static target, if monitoring camera equipment do not take place by static target cause unusual, then again context update is carried out in static target zone of living in, and in the process that static target detects, can not carry out context update.
Obviously, under the prerequisite that does not depart from true spirit of the present invention and scope, the present invention described here can have many variations.Therefore, the change that all it will be apparent to those skilled in the art that all should be included within the scope that these claims contain.The present invention's scope required for protection is only limited by described claims.
Claims (15)
1. a monitoring camera unit exception detection system is characterized in that, comprising:
Acquisition module (110) obtains the monitoring camera equipment video image of scene on every side;
Whether abnormality detection module (120) detects monitoring camera equipment by video image information and takes place unusually;
Alarm module (130) based on the abnormal conditions that take place, sends warning message to user terminal;
Wherein abnormality detection module (120) comprises motion analysis module (121) and/or static target detection module (122);
Motion analysis module (121), according to state institute's duration that moving target accounts for the ratio of whole video image and has this ratio, judge monitoring camera equipment whether take place by moving target cause unusual;
Static target detection module (122), according to state institute's duration that static target accounts for the ratio of whole video image and has this ratio, judge monitoring camera equipment whether take place by static target cause unusual.
2. a kind of monitoring camera unit exception detection system as claimed in claim 1 is characterized in that abnormality detection module (120) comprises statistical module (123), is used to add up the gray-value variation in all pixels of timing statistics inner video image.
3. a kind of monitoring camera unit exception detection system as claimed in claim 1, it is characterized in that comprising that parameter is provided with module (140), whether described parameter is provided with module (140) in prospect threshold value, abnormal motion target area threshold value, abnormal motion object time threshold value, unusual static target area threshold, the unusual static target time threshold one or more is set, take place unusually so that abnormality detection module (120) is judged monitoring camera equipment.
4. a kind of monitoring camera unit exception detection system as claimed in claim 1 is characterized in that comprising memory (150), the program in the storage monitoring camera equipment, and store background image, current frame image, warning message.
5. a kind of monitoring camera unit exception detection system as claimed in claim 1, it is characterized in that, comprise processor (160), be used for the program of execute store (150), counting and logical operation in the monitoring camera equipment carried out in the operation of each module of control monitoring camera equipment.
6. a kind of monitoring camera unit exception detection system as claimed in claim 1, it is characterized in that, if moving target accounts for the ratio of whole video image more than or equal to abnormal motion target area threshold value, and the time of this state continuance is more than or equal to abnormal motion object time threshold value, then this monitoring camera equipment taken place by moving target cause unusual.
7. a kind of monitoring camera unit exception detection method as claimed in claim 1, it is characterized in that, if static target accounts for the ratio of whole video image more than or equal to unusual static target area threshold, and the time of this state continuance is more than or equal to unusual static target time threshold, then this monitoring camera equipment taken place by static target cause unusual.
8. a monitoring camera unit exception detection method is characterized in that, described method comprises by moving object detection method and/or static target detection method, detects video monitoring equipment whether unusual step takes place; And if this monitoring camera equipment takes place unusually warning message to be sent to user terminal, the step that terminal is reported to the police;
Wherein said moving object detection method accounts for the ratio of whole video image and the time of this state continuance according to moving target, judge monitoring camera equipment whether take place by moving target cause unusual;
Described static target detection method accounts for the ratio of whole video image and the time of this state continuance according to static target, judge monitoring camera equipment whether take place by static target cause unusual.
9. a kind of monitoring camera unit exception detection method as claimed in claim 8 is characterized in that the step that obtains moving target comprises background modeling and renewal and foreground detection.
10. a kind of monitoring camera unit exception detection method as claimed in claim 9, it is characterized in that comprising the ratio that calculates moving target area and whole moving target bianry image area in the bianry image that foreground detection obtains, thereby obtain the step that moving target accounts for the whole video image scaled.
11. a kind of monitoring camera unit exception detection method as claimed in claim 10, it is characterized in that, if moving target accounts for the ratio of whole video image more than or equal to abnormal motion target area threshold value, and the time of this state continuance is more than or equal to abnormal motion object time threshold value, then this monitoring camera equipment taken place by moving target cause unusual.
12. a kind of monitoring camera unit exception detection method as claimed in claim 8, it is characterized in that, the step that obtains static target comprises the gray-value variation of all pixels in the statistical picture, extract the gray value that the frequency of occurrences is the highest in each pixel and form rest image, the gray value of all pixels of rest image and the gray value of all pixels of background image are subtracted each other, difference is formed static target more than or equal to the gray value of all pixels of prospect threshold value in the static target bianry image.
13. a kind of monitoring camera unit exception detection method as claimed in claim 12, it is characterized in that comprising the static target area that calculates in the static target bianry image and the ratio of whole static target bianry image area, thereby obtain the step that static target accounts for the whole video image scaled.
14. a kind of monitoring camera unit exception detection method as claimed in claim 13, it is characterized in that, if static target accounts for the ratio of whole video image more than or equal to unusual static target area threshold, and the time of this state continuance is more than or equal to unusual static target time threshold, then this monitoring camera equipment taken place by static target cause unusual.
15. a kind of monitoring camera unit exception detection method as claimed in claim 8 is characterized in that, if this monitoring camera equipment do not take place by static target cause unusual, context update is carried out in static target zone of living in.
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