Abstract:
An intelligent monitoring system aims to perform object surveillance and tracking, and can quickly build accurate and reliable background data in a complex image condition to achieve desired monitoring result. Based on a dynamic background and a temporary static object and user&#39;s requirements, monitoring objects in a background module can be added or deleted to match the actual background information. The whole background data can be tracked according to characteristics of a targeted object set by users, and post-processing can be done for the tracked object, such as zooming, identifying, capturing, surveillance of behaviors, and the like. Thus whether a special attention is needed for a dynamic or static object can be notified. And an alert can be issued to relevant people for timely handling.

Description:
FIELD OF THE INVENTION 
       [0001]    The present invention relates to a monitoring system and particularly to an intelligent monitoring system. 
       BACKGROUND OF THE INVENTION 
       [0002]    Crimes are rampant in many locations and countries nowadays. To attack this problem, monitoring systems are widely set up in recent years in public and private sites. For instance, most railways or high speed trains now adopt computerized automatic driving. In such autopilot public transportation systems, occurrence of obstacles on the routes is the greatest safety concern. Or in some important public sites, such as art galleries, museums, government organizations and the like, to prevent theft or disposing of unknown articles (such as explosives), a lot of manpower has to be deployed to do monitoring, or expensive theft-thwarting equipments have to be installed. To crack down traffic violations on roads, policemen have to drive hauling vehicles to do patrolling. Thus a great deal of human resources and precious time are wasted. An intelligent monitoring system is able to identify selected events and activities such as presence of obstacles, vehicle violations or thefts, and capable of instantly notifying related people or generating alarm would be very helpful. 
         [0003]    Conventional monitoring techniques often focus on object image segmentation or tracking, and comparison. System test films mostly adopt academic standard films without taking into account of actual environments. Hence how to establish backgrounds and update background information often are neglected. As an actual background often involves constantly moving objects, there is no idle duration allowing the system to capture the background, or a period of training is needed to generate the background. 
         [0004]    Moreover, most conventional techniques do not provide comprehensive exploration on static objects. For instance, National Taiwan University provides a “Background Registration” technique capable of detecting objects. It has a drawback, namely once a judgment is made, a background is saved, the saved background data remains unchanged without updated. 
         [0005]    Another conventional technique is Codebook system. It provides background learning and an image detection method. In the event that an object is static, it becomes a background. However, if the static object is an explosive and becomes the background of the monitoring system, the purpose of monitoring is futile. 
         [0006]    The two conventional approaches mentioned above still have rooms for improvement, notably: 1. No update of the background does not meet actual requirement; 2. Objects in actual sites are not always dynamic; a neglected static object should be updated to become a background (such as a vehicle parked on a road side, trash dropped on the ground by people, or the like). There are other conventional techniques that can update static objects to become the background. But the update speed is a constraint. As a result, the conventional monitoring systems still leave a lot to be desired. 
       SUMMARY OF THE INVENTION 
       [0007]    Therefore, the primary object of the present invention is to provide an intelligent monitoring system that can update images according to user&#39;s setting to judge a dynamic background and a static background. 
         [0008]    To achieve the foregoing object, the invention receives at least one input image consisting of a plurality of pixels transmitted from an image capturing unit and performs judgment. It includes a host, an intelligence judgment machine (IJM), a continuous image comparison unit and a time stabilizing unit. The host and the image capturing unit are connected. The IJM and the host are connected. The continuous image comparison unit and the time stabilizing unit are located in the IJM and connected therewith. 
         [0009]    As the continuous image comparison unit is located in the IJM and connected therewith, a threshold value Th_D is provided to be compared with the pixels. After comparison, the time stabilizing unit which contains a plurality of time threshold values gives the pixels a time value. After judgment and comparison are performed, the data of the pixels are sent to a background module, then a segmentation unit and a post-processing unit execute image post-process and monitoring operation, and filter out noises to smooth and complete the image. 
         [0010]    Therefore, judgment of the image of an object is performed in conditions in which movement of the object is continuously locked without the object being impacted and staggered. After the object is separated, it is still be tracked continuously. Thus monitoring can be accomplished. 
         [0011]    The IJM provides function of processing the input image and judging whether the input image is a static background or a dynamic background, and also judging whether the input image is a dynamic object or a static object by processing the input image through the dynamic background module and the time stabilizing unit. If the object stays for a prolonged duration, an alarm may be issued or operation of update to become background data can be executed according to user&#39;s setting requirement. 
         [0012]    Thus the IJM can distinguish whether the input image is the static background, dynamic background, dynamic object or static object. The IJM also can be set by users to perform monitoring continuously. 
         [0013]    By means of the technique set forth above, the intelligent monitoring system of the invention can provide the following advantages: 
         [0014]    1. Reliable background data can be set up quickly and accurately even in a complex and murky condition, and a dynamic background (such as swaying of the tree leaves and water ripples and the like) and a static object (such as trash or explosive) can be monitored and judged. So that swaying of the tree leaves and ripples of water do not affect monitoring quality. In addition, the static object can be differentiated to determine whether to notify relevant people to handle and to further improve monitoring quality and to save manpower and resources. 
         [0015]    2. By providing input image background update function, in addition to capable of timely adjusting variations of environment brightness, input image data that require special attention can be flexibly added or deleted according to user&#39;s requirement to match actual background information, so that monitoring and post-processing operations can be performed to alert users and monitoring suspected people and articles on the screen, and contingent plans can be established in advance to prevent abnormal conditions from taking place. 
         [0016]    The foregoing, as well as additional objects, features and advantages of the invention will be more readily apparent from the following detailed description, which proceeds with reference to the accompanying embodiments and drawings. The embodiments discussed below serve only for illustrative purpose and are not the limitations of the invention. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0017]      FIG. 1  is a schematic view of the intelligent monitoring system of the invention. 
           [0018]      FIG. 2  is a schematic view of judgment classification of the intelligent monitoring system of the invention. 
           [0019]      FIG. 3  is a schematic view of timeline of the intelligent monitoring system of the invention. 
           [0020]      FIG. 4  is a schematic view of background update of the intelligent monitoring system of the invention. 
           [0021]      FIG. 5  is the flowchart of the intelligent monitoring system of the invention. 
           [0022]      FIG. 6  is actual images at different time periods according to the intelligent monitoring system of the invention. 
           [0023]      FIG. 7  is continuous image variation values (TDMt(m)) of a sampling location according to the intelligent monitoring system of the invention. 
           [0024]      FIG. 8  is a schematic view of a static background according to the intelligent monitoring system of the invention. 
           [0025]      FIG. 9  is a background image captured on a selected site at National Taiwan University. 
           [0026]      FIG. 10  is the picture of #95 background image according to the intelligent monitoring system of the invention. 
           [0027]      FIG. 11  is test pictures of swaying of various types of tree leaves according to the intelligent monitoring system of the invention. 
           [0028]      FIG. 12  is sampling spots of test pictures (60, 80) according to the intelligent monitoring system of the invention. 
           [0029]      FIG. 13  is a distribution chart of local continuous image variation values (TDMt(m)) of the sampling spots (60, 80) according to the intelligent monitoring system of the invention. 
           [0030]      FIG. 14  is a local vector distribution chart of the sampling spots (60, 80) according to the intelligent monitoring system of the invention. 
           [0031]      FIG. 15  is Benchmark picture segmentation comparisons according to the intelligent monitoring system of the invention. 
           [0032]      FIG. 16  is picture segmentation comparisons of moving people according to the intelligent monitoring system of the invention. 
       
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
       [0033]    Please refer to  FIGS. 1 and 2  for the system schematic view and judgment classification of the invention. The invention receives at least one input image  11  consisting of a plurality of pixels (not shown in the drawings) transmitted from an image capturing unit  10  and performs judgment. It includes a host  20 , an intelligence judgment machine (IJM)  30 , a continuous image comparison unit  31 , a time stabilizing unit  32 , a background module  40 , a segmentation unit  50  and a post-processing unit  60 . The image capturing unit  10  is a monitor and connected to the host  20 . The IJM  30 , background module  40 , segmentation unit  50  and post-processing unit  60  are located in and connected to the host  20 . The continuous image comparison unit  31  and the time stabilizing unit  32  are located in and connected to the IJM  30 . The background module  40  has a dynamic background module  41  and a static background module  42 . 
         [0034]    Referring to  FIG. 2 , the input image  11  is divided into a dynamic background  111 , a static background  112 , a dynamic object  113  and a static object  114 . The static background  112  and the static object  114  are transmitted to the static background module  42  and saved. The dynamic background  111  and the dynamic object  113  are transmitted to the dynamic background module  41  and saved. The number of the dynamic object  113  saved in the dynamic background module  41  is three times of the number of the static object  114  saved in the static background module  42 , the background module  40  transmits to the segmentation unit  50  to segment the image. The segmental images are transmitted to the dynamic background module  41  and the post-processing unit  60  to facilitate judgment and post-processing of the input image  11 . 
         [0035]    Also refer to  FIG. 3  for the timeline of the invention. The pixels of the input image  11  at time t and location m are named Xt(m) consisting of k. (by number). As shown by the equation (3-1) below, if a three-dimensional color space is adopted, k equals to 3; if a grey scale is adopted, k equals to 1. 
         [0000]        x   t ( m )=( x   t   c1 ( m ), x   t   c2 ( m ), . . .  x   t   ck ( m ))   (3-1) 
         [0036]    The continuous image comparison unit  31  defines a continuous image variation value (TDMt(m)) to indicate a variation degree among continuous images, and also defines a threshold value Th_D to compare variations of the pixels. As shown in the equation (3-2) below, when the variation is smaller than the threshold value Th_D, the continuous image variation value (TDMt(m)) is 0, otherwise, is 1. 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       TDM 
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                       m 
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                   = 
                   
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                                       ( 
                                       m 
                                       ) 
                                     
                                   
                                 
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                             ≤ 
                             Th_D 
                           
                         
                       
                       
                         
                           1 
                         
                         
                           otherwise 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   
                     3 
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         [0037]    Also referring to  FIGS. 1 ,  2  and  3 , the time stabilizing unit  32  gives STt(m) to the pixels to represent time. If the continuous image variation value (TDMt(m)) is 0, STt(m) is 1; if the continuous image variation value (TDMt(m)) is 1, it means that changes happened to the pixels, then STt(m) is 0, as shown by the equation (3-3) below: 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       ST 
                       t 
                     
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                       m 
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                                 TDM 
                                 t 
                               
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         [0038]    As the time stabilizing unit  32  has a plurality of time threshold values (not shown in the drawings), defined a first time threshold value as (Th_ST 1 )  321  and a second time threshold value as (Th_ST 2 )  322 , and set (Th_ST 1 )  321 &lt;(Th_ST 2 )  322 , and (Th_ST 1 )  321  is ⅓ of (Th_ST 2 )  322 , the relationship between STt(m) and (Th_ST 1 )  321  can determine whether the pixels is to be classified as the dynamic background  111  or the static background  112 . 
         [0039]    When STt(m) reaches the first time threshold value (Th_ST 1 )  321 , the input image  11  could be either the static object  114  or the static background  112 . Hence when the information of the input image  11  stored in the static background module  42 , and the greater the value of STt(m) becomes, the pixels becomes more stable, and the static object  114  is more likely to become the static background  112 . 
         [0040]    When STt(m) is between the first time threshold value (Th_ST 1 )  321  and the second time threshold value (Th_ST 2 )  322 , it means that the static background  112  is built gradually. Judged by the invention, and classified as the static object  114  and incorporated with the pixel area value of the static object  114 , the static object  114  can be targeted. Incorporating with the second time threshold value (Th_ST 2 )  322 , judgment of the static object  114  can be made. 
         [0041]    Thus, when the static object  114  stays at the input image  11  for a duration exceeding an expected value preset by users, a corresponding process set by the users will be generated, such as alert, alarm or calling police. If the static object  114  is a suspected article, an alarm or calling police is issued. If it is an ordinary article uninterested to the users, it is updated to become the static background  112 . 
         [0042]    When STt(m) is smaller than the first time threshold value (Th_ST 1 )  321 , although the continuous image variation value (TDMt(m)) is 0, to judge whether the pixels are dynamic object  113  or the dynamic background  111  is still not possible; but through observation and inference, the possibility of being the dynamic background  111  is highest, because by observing the dynamic background  111  (such as swaying of the tress leaves, rippling of water or the like) the frequency of swaying or rippling is quite high, hence STt(m) of the tree leaves or water ripples is at a shorter interval than the one generated by people walking. Thus given a smaller STt(m), and the continuous image variation value (TDMt(m)) being 1, based on the pixel data of the input image  11  the condition is sufficient to judge whether the dynamic background  111  exists. To avoid erroneous judgment, two more judgment conditions ought to be added to differentiate the dynamic object  114  and the dynamic background  111 . A candidate of the dynamic background  111  can be decided once any one of the two judgment conditions is met. 
         [0043]    The first judgment condition is time interval. When STt(m) is too low, namely STt(m) is smaller than the first time threshold value (Th_ST 1 )  321 , the IJM automatically clears the data. If data clearing is repeated frequently, classification of the dynamic background  111  is made. 
         [0044]    The second judgment condition is the area size of the pixels of the dynamic object  113 . If the area of the dynamic object  113  is smaller than a preset value, the condition is met. 
         [0045]    In the event that the above two judgment conditions are met, the pixels of the current input image  11  are saved in a temporary dynamic background (not shown in the drawings). If the appearing frequency is excessive, the temporary dynamic background is defined as the dynamic background  111 . 
         [0046]    Also refer to  FIG. 4  for update of the background. The pixels of the input image  11  are compared with the background module  40  of each layer by applying the principle of Euclidean distance. Assumed that there are two points a=[a 1 , a 2 , . . . , ad] and b=[b 1 , b 2 , . . . ,bd] in a d dimension space, the Euclidean distance can be obtained by the equation (3-4) below: 
         [0000]    
       
         
           
             
               
                 
                   
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         [0047]    Then the space distance of the two points of the pixels can serve as the comparison condition. Given a point BG 1 ( m ) on the static background  112 , points BG 2 ( m ) . . . BGN(m) belong to the dynamic background  111 . When comparison of the pixels of the input image  11  matches, the information saved in the background module  40  are updated proportionally. 
         [0048]    The updated background information is transmitted to the segmentation unit  50  and the post-processing unit  60  to perform image segmentation and post-processing operations. The segmental images are sent respectively to the dynamic background module and the post-processing unit, and are monitored continuously through setting of the IJM  30 . 
         [0049]    Refer to  FIG. 5  for the flowchart of the invention. Steps  100  and  102 : Start process, and the image capturing unit  10  generates the input image  11 ; step  104 : the input image  11  is transmitted to the continuous image comparison unit  31  which has the threshold value Th_D to be compared with the input image  11 ; 
         [0050]    Step  106 : the compared input image  11  is transmitted to the time stabilizing unit  32  which provides STt(m); As the time stabilizing unit  32  has the first time threshold value (Th_ST 1 ) 321  and the second time threshold value (Th_ST 2 ) 322 , step  108 : when STt(m) is greater than the first time threshold value (Th_ST 1 ) 321 , transmit to the static background module  42  and set a temporary static background (not shown in the drawings) and a temporary static stabilizing time (not shown in the drawings); 
         [0051]    Step  110 : when STt(m) is greater than the second time threshold value (Th_ST 2 ) 322 , classify the static background  112  at step  112 ; when STt(m) of another pixel is not greater than the second time threshold value (Th_ST 2 ) 322 , proceed step  114 , and classify the static object  114  and proceed image monitoring, learning or issue alarm. 
         [0052]    Step  108 : when STt(m) is smaller than the first time threshold value (Th_ST 1 ) 321 , proceed step  116 : transmit the pixel to the dynamic background module  41 ; as the IJM  30  has a preset frequency indicator and a segmental area value, when the IJM  30  automatically clears the pixel at a frequency higher than the frequency indicator, the pixel is classified as the temporary dynamic background at step  118 , and the pixel being automatically cleared is given a counter (not shown in the drawings); the temporary dynamic background has a set frequency threshold value (not shown in the drawings); when the number in the counter is greater than the frequency threshold value, the temporary dynamic background is defined as the dynamic background  111 . In the event that the automatic clearing frequency of the pixel is lower than the frequency indicator, proceed step  120 : classify the dynamic object  113  and proceed image monitoring, learning or issue alarm. 
         [0053]    After the background information is judged and classified, enter step  122 : by means of the principle of Euclidean distance, process in each background information, and update the background according to the alteration ratio. Get information after the background has been updated, and transmit to step  124  to segment the image through the segmentation unit  50 ; then transmit respectively to steps  126  and  128 . 
         [0054]    Step  126 : when the area value of the pixel is smaller than the set segmental area value, it becomes the dynamic background  111  at step  118  to update the background module  40  timely. If the area value of the pixel is greater than the set segmental area value, proceed step  120 . 
         [0055]    Step  128 : the segmental image data is transmitted to the post-processing unit  60  to perform image post-processing to facilitate image integration and identification. 
         [0056]    Refer to  FIGS. 3 and 6  for the timeline and the actual images at different time periods. A scene of people walking frequently is captured. A selected location ( 120 ,  240 ) is targeted where people pass through frequently. By observing alterations of TDM(m) of each pixel in the picture, capture a time when STt(m) exceeds the first time threshold value (Th_ST 1 ) 321 , and the pixel is saved for judgment and analysis. In this embodiment, a colored RGB image is input, and the image size is 240×320. 
         [0057]    Also refer to  FIG. 7  for the continuous image variation values (TDMt(m)) of a sampling location according to the intelligent monitoring system of the invention. The sampling location is at (120, 240) for images numbered from  25  to  39  in  FIG. 6 . The continuous image variation values (TDMt(m)) of number  28  to  30 , and  35  to  37  in  FIG. 6  is 0. 
         [0058]    Refer to  FIGS. 3 ,  6  and  8  for the static background of the invention. Picture # 55  in  FIG. 6  has continuous image variation value (TDMt(m)) of 1. Then the pictures thereafter until # 70  has continuous image variation values (TDMt(m)) of 0. Meanwhile STt(m) is greater than the first time threshold value (Th_ST 1 ) 321 . Hence the pixel is saved as the temporary static background until the image # 90  which has the continuous image variation value (TDMt(m)) of 1. Then the temporary static background serves as the background data, and STt(m) is saved and set as the temporary static stabilizing time. Not Only the color information and time are saved. Thus there are not too much processing amount and memory space being wasted. 
         [0059]    When STt(m) is between the first time threshold value (Th_ST 1 ) 321  and the second time threshold value (Th_ST 2 ) 322 , STt(m) is compared with the temporary static stabilizing time. If STt(m) is greater than the temporary static stabilizing time, the old temporary static background and the temporary static stabilizing time are replaced to become the current temporary static background and the temporary static stabilizing time. By means of such a technique, the image being built is more reliable and like the actual background. When STt(m) is increased to the second time threshold value (Th_ST 2 ) 322 , it does not increase anymore, as a stable background is established. Thereafter the second time threshold value (Th_ST 2 ) 322  serves as the condition of background update. 
         [0060]    Refer to  FIG. 9  for a captured background image on a selected site at National Taiwan University. Test result in  FIG. 9  shows that adopted the method of Background Registration on a people moving condition, even if the image has reached picture No.  648 , the background still cannot be established (the black portion). 
         [0061]    Refer to  FIG. 10  for the background image of image # 95  according to the invention. Through the invention, complete background information is obtained less than 4 seconds (for picture # 95 ). Hence the problem of unable to establish the background in the frequent moving condition (such as moving of people) is improved. 
         [0062]    Also referring to  FIGS. 11 through 14 , with  FIG. 11  showing tested pictures of various types of swaying tree leaves,  FIG. 12  showing a sampling point ( 60 ,  80 ) of the tested pictures,  FIG. 13  showing the local distribution of continuous image variation values (TDMt(m)) of the sampling point ( 60 ,  80 ), and  FIG. 14  showing the local vector distribution of the sampling point ( 60 , 80 ). To define variation characteristics of the dynamic background  113 , referring to the pictures of  FIGS. 11 through 14 , based on a single pixel in the pictures of swaying tree leaves, the swaying of the tree leaves appears intensively and repeatedly, and is regular based on statistics and observation for a prolonged period of time. According to the local distribution of the continuous image variation values (TDMt(m)) of the sampling point shown in  FIG. 13 , STt(m) of the local tree leaves is relatively small (smaller than the first time threshold valve (Th_ST 1 ) 321 ). Namely, one continuous image variation value (TDMt(m)) of 1 exists for a small interval. Meanwhile, the input image  11  of the tree leaves is defined as the dynamic object  113 . The allocated area in  FIG. 14  is the value corresponding to the continuous image variation value (TDMt(m)). In such a condition, the dynamic object  113  could be the dynamic background  111  wanted. Also referring to  FIG. 5 , add two more judgment conditions, namely the frequency indicator and the segmentation area value to differentiate the dynamic object  113  and the dynamic background  111 . Thus when these two judgment conditions are met, the input image  11  is saved in a temporary dynamic background. 
         [0063]    Also refer to  FIGS. 15 and 16  for Benchmark picture segmentation comparison and picture segmentation comparison of moving people according to the intelligent monitoring system of the invention. The first row is input test images, second row (A) is the method provided by National Taiwan University, third row (B) adopts CodeBook method, and fourth row (C) adopts the method of the invention. In  FIG. 15 , the segmentation results of the invention and the CodeBook method are about the same, but the invention consumes much less memory and processing amount. Noise also improves greatly after post-processing. In  FIG. 16 , the first column is taken from picture No.  25  in  FIG. 6 . Adopted CodeBook approach, saving and training of the background are still under the processing stage. Hence there is no output in the first frame of (B) in  FIG. 16 , while the background has been updated in the invention, as shown in (C) of  FIG. 16 . 
         [0064]    As a conclusion, the invention can rapidly establish reliable background information in a complex image environment to allow users to perform monitor according to wanted image characteristics, and do post-processing for the monitored images, such as zooming, identifying, capturing or surveillance of actions, and can transfer uninterested image information to become dynamic background or static background. Therefore, through the images occurrence of abnormal conditions can be known and alarm can be generated to allow users to take responsive actions timely. 
       LEGEND FOR FIG.  1   
       [0000]    
       
           10 —Image capturing unit 
           31 —Continuous image comparison unit 
           32 —Time stabilizing unit 
           41 —Dynamic background module 
           42 —Static background module 
           50 —Segmentation unit 
           60 —Post-processing unit 
       
     
       LEGEND FOR FIG.  2   
       [0000]    
       
           11 —Input image 
           111 —Dynamic background 
           112 —Static background 
           113 —Dynamic object 
           114 —Static object 
       
     
       LEGEND FOR FIG.  4   
       [0000]    
       
           11 —Input image 
       
     
       LEGEND FOR FIG.  5   
       [0000]    
       
           100 —Start 
           102 —Generate input image through an image capturing unit 
           104 —Perform image comparison 
           106 —Provide STt(m) 
           108 —Greater than the first time threshold value (Th_ST 1 )? 
           110 —(Yes) Is STt(m) greater than the second threshold value (Th_ST 2 )? 
           112 —Static background 
           114 —(No) Static object 
           116 —Greater than frequency indicator? 
           118 —(Yes) Dynamic background 
           120 —(No) Dynamic object 
           122 —Update background 
           124 —Segment image 
           126 —Smaller than segmentation area value? 
           128 —Image post-processing