# Patent

## Title
Video stabilizing method and system using dual-camera system

## Abstract

The present invention discloses a video stabilizing method. The method may comprising the steps of: capturing a low-spatial-resolution image ILt by a first camera and a high-spatial-resolution image IHt by a second camera which is synchronous with the first camera for capturing an image of a moving target; determining a target region IL—tart including the moving target in the low-spatial-resolution image ILt , and obtaining an output image Ioutt of the high-spatial-resolution image IHt corresponding to the target region IL—tart; generating a registration model MLHt between the low-spatial-resolution image ILt and the high-spatial-resolution image IHt ; and inpainting the output image Ioutt based on the registration model MLHt and the high-spatial-resolution image IHt to complete the output image Ioutt. The present invention further discloses a video stabilizing system using a dual-camera system.

## Background
CROSS-REFERENCE TO RELATED APPLICATIONS
This application claims priority to, and benefits of Chinese Patent Application No. 200910088991.2, filed with the State Intellectual Property Office, P.R.C. on Jul. 17, 2009, the entire contents of which are incorporated herein by reference.
BACKGROUND OF THE INVENTION
1. Field
The present invention relates to computer vision, more particularly to a video stabilizing method and a video stabilizing system for stabilizing videos with high-spatial-resolution using a dual-camera system, such as a static-active camera system or an active-active camera system.
2. Description of the Related Art
With the development of computer vision and the enhancement of worldwide attention on security, intelligent visual surveillance is now being paid more attention than ever before. And the related technology has been gradually applied for military and civilian purposes. In earlier applications, the intelligent degree is low. Therefore, security surveillance is mainly determined manually for the event occurred in the monitored videos with low degrees of reliability and automation. And it is mainly used for criminal evidence collection in visual surveillance. However, presently, intelligent visual surveillance pays more attention to criminal early warning to reduce occurrence of criminal cases.
In traditional surveillance, a single static camera has been unsuitable for modern surveillance requirement. Firstly, because the single static camera has a fixed viewpoint and when the moving target moves out of the viewing field, information of the monitored target will be completely lost. Secondly, due to the conflict between image resolution and the size of the viewing field, the viewing field will be relatively small if the required image resolution is high, especially for long-distance surveillance.
Therefore, a single active camera may be adopted to solve the conflict between the image resolution and the size or dimension of the viewing field. However, during the active tracking of the moving target, due to the continuous changes of the camera parameters, the background and the target in the image will move accordingly, so that it is very difficult to accurately predict the movement of the target. On the other hand, the camera can not be accurately controlled in addition to the difficult estimation of the camera movement time, so that the target may easily lost in the image or video whether an computer-controlled automatic tracking method or a manual tracking method is adopted. Therefore, robustness for capturing high-spatial-resolution video of a target by a single active camera is low.
Thus, video stabilization shall be performed to make the moving target in the video run more smoothly with improved visualization effect. In addition, after video stabilization, desired features may be easily extracted from the monitored target. Further, computer vision researches may be performed on the videos after video stabilizing, such as gesture recognition, behavior and gait analysis, or object identification.
In the case of long-distance surveillance, high-spatial-resolution frames of the interested targets captured may need to perform stabilization to increase video visualization effects. Generally, because there is a long distance between the target and the high-spatial-resolution camera, minor trembling of the camera may lead to severe changes of the moving targets in the image, thus the video visualization effect is inferior.
Further, for long-distance high-spatial-resolution surveillance, it is challenging to obtain a stabilized video with high-spatial-resolution, because there are problems such as image blurriness, and incompleteness in the acquired high-spatial-resolution video. The image blurriness is mainly caused by camera shaking, because the camera has to be operated in a high-speed mode to ensure the effective tracking of the active camera to the moving target, which may cause image blurriness in the video. In a prior dual-camera monitoring system, although a low-spatial-resolution camera is used for preventing the moving target from being lost, it is difficult to ensure each high-spatial-resolution frame to completely contain the moving target. Because each action of the active camera does need a certain time period for response which is hard to be accurately determined, thus there is always overtuning such as overshooting or undershooting due to the time delay in mechanical movement during controlling of the camera. Further, there may be a dithering region in the obtained video thereof. If the moving target runs at overspeed, there is an over-controlling frequency of the active camera which may result in inter-frame dithering.
SUMMARY
In viewing thereof, the present invention is directed to solve at least one of the problems existing in the prior art. Accordingly, a video stabilizing method using a dual-camera system may be provided, in which long-distance surveillance may be performed via a high-spatial-resolution video which is stabilized. Further, a video stabilizing system using a dual-camera system may be provided, which may improve video surveillance via the high-spatial-resolution video which is stabilized.
According to an aspect of the present invention, a video stabilizing method using a dual-camera system may be provided, comprising the following steps of: 1) capturing a low-spatial-resolution image ILt by a first camera for monitoring a panoramic area and a high-spatial-resolution image IHt by a second camera which is synchronous with the first camera for capturing an image of a moving target where ILt, IHt represent the low-spatial-resolution image and the high-spatial-resolution image at the tth frame respectively; 2) determining a target region IL—tart including the moving target in the low-spatial-resolution image ILt, and obtaining an output image Ioutt of the high-spatial-resolution image IHt corresponding to the target region IL—tart, 3) generating a registration model MLHt between the low-spatial-resolution image ILt and the high-spatial-resolution image IHt, and 4) inpainting the output image Ioutt based on the registration model MLHt and the high-spatial-resolution image IHt to complete the output image Ioutt.
According to another aspect of the present invention, a video stabilizing system using a dual-camera system may be provided, comprising a first camera unit, a second camera unit and a controller. The first camera unit may monitor a panoramic image and obtaining a low-spatial-resolution image ILt where ILt may represent the low-spatial-resolution image at the tth frame. The second camera unit may capture a moving target and sample a high-spatial-resolution image IHt, where IHt may represent the high-spatial-resolution image at the tth frame, the second camera unit being synchronous with the first camera unit. And the controller may receive the low-spatial-resolution image ILt and the high-spatial-resolution image IHt, output an output image Ioutt of the high-spatial-resolution image IHt corresponding to a target region IL—tart where the moving target may be located in the low-spatial-resolution image ILt, generate a registration model MLHt between the low-spatial-resolution image ILt and the high-spatial-resolution image IHt, and inpaint the output image Ioutt based on the registration model MLHt and the high-spatial-resolution image IHt to complete the output image Ioutt.
According to the present invention, image registration problems between videos with different spatial resolutions may be solved smoothly. In addition, four following types of image completion strategies may be proposed: high-spatial-resolution image inpainting; high-spatial-resolution background image inpainting; foreground image inpainting and low-spatial-resolution image inpainting. Thus, current high-spatial-resolution information and historic high-spatial-resolution information may be fully used to inpaint the target video. Through the above processing, the video may be used for collection of criminal evidences, storage of surveillance records, behavioral analysis of moving targets, etc. Experimental results have shown that the proposed stabilization and completion algorithms work well.
Additional aspects and advantages of the embodiments of present invention will be given in part in the following descriptions, become apparent in part from the following descriptions, or be learned from the practice of the embodiments present invention.



BRIEF DESCRIPTION OF THE DRAWINGS
These and other aspects and advantages of the invention will become apparent and more readily appreciated from the following descriptions taken in conjunction with the drawings in which:
FIG. 1 is a flow chart of a video stabilizing method according to an embodiment of the present invention;
FIG. 2 is an intensity adjusting schematic view of a video stabilizing method according to an embodiment of the present invention, in which FIG. 2(a) is a fitting diagram of a piece-wise linear model according to an embodiment of the present invention, FIG. 2(b) is a histogram of an original low-spatial-resolution image according to an embodiment of the present invention, FIG. 2(c) is a histogram of a high-spatial-resolution image according to an embodiment of the present invention, and FIG. 2(d) is a histogram of an adjusted low-spatial-resolution image according to an embodiment of the present invention; and
FIG. 3 is a block diagram for computing an optical flow field according to an embodiment of the invention, in which FIG. 3(a) is a block diagram for computing an inter-frame high-spatial-resolution optical flow field according to an embodiment of the present invention, FIG. 3(b) is a block diagram for computing an inter-frame low-spatial-resolution optical flow field according to an embodiment of the present invention.



Figure Numeral Designation:
ILt: a low-spatial-resolution image at the tth frame;
IHt: a high-spatial-resolution image at the tth frame;
ILBt: a low-spatial-resolution background image at the tth frame;
IHBt: an updated high-spatial-resolution background image corresponding to ILBt at the tth frame;
IL—tart: a target region in the low-spatial-resolution image at the tth frame;
IL—adjt: an image after adjusting intensity of ILt;
IH—adjt: an image of IHt transformed by MLH1t;
Ioutt: an output image at the tth frame;
ko: a magnifying factor of the output image relative to the low-spatial-resolution target region;
MLH1t: a rough registration model between ILt and IHt at the tth frame;
MLH2t: a refined registration model between ILt and IHt at the tth frame;
MLHt: a final registration model between ILt and IHt at the tth frame;
Mjt: a transforming model from the jth high-spatial-resolution image to the ith high-spatial-resolution image;
SPLi: a low-spatial-resolution fixed reference frame (40×40) containing only the foreground target in the ith frame;
SPHi: a high-spatial-resolution reference frame (200×200) corresponding to SPLi containing only the foreground target in the ith frame;
R1: an image region of the output image inpainted in step 4.2.1);
R2: an image region of the output image inpainted in step 4.2.2);
R3: an image region of the output image inpainted in step 4.2.3); and
R4: an image region of the output image inpainted in step 4.2.4).
DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION
Reference will be made in detail to embodiments of the present invention. The embodiments described herein with reference to drawings are explanatory, illustrative, and used to generally understand the present invention. The embodiments shall not be construed to limit the present invention. The same or similar elements and the elements having same or similar functions are denoted by like reference numerals throughout the descriptions.
According to the present invention, a novel framework to stabilize and complete such video using a dual camera system is proposed, of which one camera serves as a panorama with low spatial resolution, and the other is an active camera such as a PTZ (pan-tilt-zoom) camera capturing high-spatial-resolution images. As the discrepancy in resolution between two synchronized videos will increase the registration difficulty, we propose a three-step stabilization approach to deal with this problem at each frame. In order to make full use of the high-spatial-resolution information, four types of image completion strategies are designed: current high-spatial-resolution image inpainting; high-spatial-resolution background model inpainting; sample patch with motion field based foreground inpainting and current scaled low-spatial-resolution image inpainting.
To be specific, the general inventive concept of the present invention is as follows.
The first aspect relates to registration between images with different spatial resolutions. And the present invention combines feature-based approach and pixel-based approach, and a three-step registration method is designed which can effectively achieves registration of images with different spatial resolutions.
The second aspect relates to strategies for inpainting high-spatial-resolution information. Firstly, a region with high-spatial-resolution information of the current frame is directly inpainted. For the un-inpainted regions, historic high-spatial-resolution information is used for further inpainting. And the inpainting concept is as follows: foreground and background image segmentations are performed for each pixel in the viewing field to be inpainted. The background layer may be inpainted by generating a high-spatial-resolution background model. And the foreground layer may be inpainted by a method based on reference sample patch and relative motion field.
The third aspect relates to video or image postprocessing. If neighboring pixels use inpainting information with different spatial resolutions, unsmoothness in a single frame image may occur. Therefore, image postprocessing may be necessary to improve visualization effects.
Further, according to an embodiment of the present invention, a static-active or active-active camera system is used to provide a video stabilizing method or system for video stabilization, in which a camera may be used as a low-spatial-resolution camera for real time tracking to the moving target, with another camera being used as a high-spatial-resolution camera for active tracking of the moving target to obtain information of the moving target. For the active-active camera system, on one hand, an active camera may be used as a static camera for flexible purpose. On the other hand, in consideration of system symmetry, if both active cameras are consistent, it would be very convenient for switching the active cameras based on different missions.
In all, the video stabilization and completion has the following goals: 1) to keep the interesting target near the center of the output image; 2) to keep intact the spatial image content of the output image; and 3) the output image contains as much high-spatial-resolution information as possible, which will be achieved by the solution as described hereinbelow.
In the following, the video stabilizing method according to an embodiment of the present invention will be described in detail with reference to the accompanying figures, in which FIG. 1 is a flow chart of the video stabilizing method according to an embodiment of the present invention. The video stabilizing method may include the steps as follows.
Step S101: A low-spatial-resolution image ILt is captured by a first camera for monitoring a panoramic area and a high-spatial-resolution image IHt is captured by a second camera which is synchronous with the first camera for capturing an image of a moving target, where ILt, IHt may represent the low-spatial-resolution image and the high-spatial-resolution image at the tth frame respectively. In this step, the first and second cameras may use a PTZ (pan-tilt-zoom) camera respectively.
Step S102: Foreground image and background image may be segmented in the low-spatial-resolution image ILt.
Step S103: A target region IL—tart is determined based on the low-spatial-resolution image ILt and the foreground image of the obtained low-spatial-resolution image ILt. Firstly, a Mean-shift tracking algorithm is used for obtaining the track of the interested target. According to an embodiment of the present invention, considering the smoothing requirement, centers of the target in the neighboring frames, such as 50 frames, are averaged for smoothing purpose. According to an embodiment of the present invention, a rectangular region IL—tart may represent the target region with a dimension of 64 pixels×48 pixels. The center of the region is the target center after smoothing. However, it should be noted that the dimension of the target region IL—tart may be adjusted as required, which also falls within the scope of the present invention. During target tracking, a background model is obtained by a running average method with an updating coefficient of 0.05, and a Gaussian background may be generated for the low-spatial-resolution video to obtain the foreground region and the background region ILBt in the low-spatial-resolution image ILt.
Step S104: The viewing field of the output image Ioutt is determined. Because the present invention is designed for long-distance surveillance, the dimension of the output image Ioutt may be set to be ko times as large as that of IL—tart. According to an embodiment of the present invention, ko=5.
Step S105: Registration may be performed between video images with different spatial resolutions. That is to say, a mapping model is obtained between the high-spatial-resolution image IHt and the low-spatial-resolution image ILt.
Presently, there are still no accurate definitions for video stabilization. And the evaluation strategies for video stabilization at present mainly lie in that the interested target is located near the image center and the movement of the moving interested target is as continuous and smooth as possible. For achieving both purposes as described hereinbefore, the region in the high-spatial-resolution image IHt corresponding to the target region in the low-spatial-resolution image ILt is outputted as an output image Ioutt. Then, a mapping model between the high-spatial-resolution image IHt and the output image Ioutt is calculated. During the calculation of the registration model between the high-spatial-resolution image IHt and the output image Ioutt, there is only a scale transforming relationship between the output image Ioutt and the target region IL—tart, thus only the transforming model between the high-spatial-resolution image IHt and low-spatial-resolution image ILt is needed. Further, because there is a short distance between the first and second cameras, the distance difference of the first and second cameras relative to the monitored scene may be omitted. Therefore, according to an embodiment of the present invention, a registration model or an affine model may be used as the transforming model between the high-spatial-resolution image IHt and low-spatial-resolution image ILt.
Firstly, a rough registration or affine model may be estimated by using a feature-point matching method, and then the intensity of the low-spatial-resolution image ILt may be adjusted by the rough affine model, and finally a refined or accurate affine model may be estimated by a pixel-based direct method. There is a requirement of accuracy between the low-spatial-resolution image and the high-spatial-resolution image. And the accuracy of the model can ensure the low-spatial-resolution panoramic image to be used as a bridge for the high-spatial-resolution images at different time. And when the high-spatial-resolution information is invalid, the panoramic image after interpolation may be used for inpainting. The accuracy of the model is ensured by the following two-step registration. When a fixed point in a scene is selected, the point which is transformed from the panoramic image to the high-spatial-resolution image via the affine model has a minimal difference with the actual coordinates of the point at each time. And the smoothness of the model is reflected by the minimal difference between the point coordinates in the high-spatial-resolution image affined at different time. According to an embodiment of the present invention, to reduce the computing load of the registration model, the image of each frame is transformed into a gray image.
According to an embodiment of the present invention, the step of generating the mapping model or registration model between the high-spatial-resolution image IHt and the low-spatial-resolution image ILt may include the following steps.
Step S201: The rough affine model MLH1t is estimated by a feature-point matching method. Because the magnification factor between the high-spatial-resolution image IHt and the low-spatial-resolution image ILt is unknown, feature point operator with scale invariability is selected. According to an embodiment of the present invention, feature point operator SURF (Speeded Up Robust Feature) is adopted, which is one of the most widely used descriptors presently. To reduce the computing load, only the feature points in the target region IL—tart of the low-spatial-resolution image ILt are calculated. During feature matching, matched feature point pairs are obtained via an approximate nearest neighbors (ANN) method. If the matched pair number is less than 10, MLH1t will be invalid, and MLHt will not be calculated. Otherwise, the rough affine model MLH1t using the feature points will be estimated as follows.
The SURF matched feature point pairs between the target region IL—tart in high-spatial-resolution image IHt and the low-spatial-resolution ILt may be designated by {(xi1, yi1)εILt, (xi2, yi2)εIHt}, i=1, 2, . . . , n, and the affine transforming matrix is:




  
    
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in which,

Step S202: Intensity thereof is adjusted to obtain a mapping model MI(k) between the intensities of the two images. According to an embodiment of the present invention, a polygon with a minimum size including all the feature points is estimated for the matched feature point pairs set in the two images, and the pixel intensities inside the polygon are sampled to determine an intensity mapping relationship using a method similar to the histogram equalization method. For the mapping model, there may be many options. According to an embodiment of the present invention, a piece-wise linear model is adopted. Firstly, the intensity histograms are accumulated to obtain an accumulated histogram:




  
    
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in which Accu(k) is a monotonic increasing function of variable k. And a three-piece-wise linear model is selected as the mapping model. And the intensity sets are as follows:

K1={k:0≦Accu(k)<0.05}

K2={k:0.05≦Accu(k)<0.95}

K3={k:0.95≦Accu(k)≦1}

And the intensity mapping function MI(k) between ILt and IHt is linearly fitted by the following objective function:




  
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in which Accu1 and Accu2 represent the accumulated distribution histograms on the two images, ILt and IHt, respectively. To ensure intensity continuity and effectiveness, the remaining two parts of intensity sets K1 and K2fit a linear model respectively so that MI(0)=0 and MI(255)=255.
And ILt may be adjusted by the mapping model MI(k) to obtain IL—adjt. As shown in FIG. 2, FIG. 2(a) is a fitting diagram of a piece-wise linear model according to an embodiment of the present invention, FIG. 2(b) is a histogram of an original image with low-spatial-resolution according to an embodiment of the present invention, FIG. 2(c) is a histogram of an image with high-spatial-resolution according to an embodiment of the present invention, and FIG. 2(d) is a histogram of an adjusted image with low-spatial-resolution according to an embodiment of the present invention. From the figures, the adjusted histogram is more similar to the histogram of the high-spatial-resolution image, so that the refined registration via the direct pixel-based method in the following may be performed.
Step S203: Refined affine model is estimated directly via the pixel-based method. The rough affine model MLH1t between the two images, and ILt and IHt, and the mapping model MI(k) therebetween is obtained via the feature-based rough registration. Firstly, IHt is transformed by MLH1t in the present invention to obtain IH—adjt. Then M0=I3×3 is used as an initial value for iteratively estimating a more accurate mapping model. And the optimal objective is the minimum value of the following formula:




  
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in which f(xi, M) is a homogeneous coordinates conversion function, and the optimization problem is iteratively solved by the gradient based Hessian matrix. And f(xi, M) is calculated as follows:

(x′, y′, 1)T=M(x, y, 1), f(x, y, M)=(x′, y′)

If MI satisfies either of the following two conditions, MI will be considered to be invalid, and the calculation of MLHt will be skipped.

    
    
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in which MI=[RM tM], [R2×2M t2×1M] is the first two rows of MI. If both MI and MLH1t are valid, MLH2t=MLH1tMrefinedt, otherwise the following steps will be skipped and MLHt will not be calculated.
Step S203: Model Smoothing.
Firstly, a mapping model Mji between two high-spatial-resolution images is obtained. Because there are many problems for directly solving the mapping model between the two high-spatial-resolution images, the high-spatial-resolution image region will be firstly filtered by the background region in the obtained panoramic image sequence, with the background portion remained, so that the registration error caused by the foreground movement will be eliminated. Then, the SURF feature points extracted during the rough matching is used to match the feature points in the background region, and the transforming model is estimated. Similarly, if the number of the feature point pairs is less than 10, the mapping model Mji will be invalid.
And a smoothing model MLHt is solved via the following formula:




  
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in which ωi is the Gaussian weight, N=5,




  
    
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        1
        
          
            
              2
              ⁢
              π
            
          
          ⁢
          σ
        
      
      ⁢
      
        e
        
          
            
              (
              
                i
                -
                t
              
              )
            
            2
          
          
            2
            ⁢
            
              σ
              2
            
          
        
      
      ⁢
      
        
      
      ⁢
      
        (
        
          σ
          =
          1.5
        
        )
      
    
  
  ,




δi is the characteristic function satisfying:





  
    δ
    j
  
  =
  
    {
    
      
        
          
            1
            ,
            
              if
              ⁢
              
                
              
              ⁢
              
                M
                j
                i
              
              ⁢
              
                
              
              ⁢
              and
              ⁢
              
                
              
              ⁢
              
                M
                
                  LH
                  ⁢
                  
                    
                  
                  ⁢
                  2
                
                i
              
              ⁢
              
                
              
              ⁢
              are
              ⁢
              
                
              
              ⁢
              both
              ⁢
              
                
              
              ⁢
              valid
            
          
        
      
      
        
          
            0
            ,
            otherwise
          
        
      
    
  




Finally, the relative blurriness bt of the current frame may be calculated as follows:




  
    b
    t
  
  =
  
    1
    
      
        ∑
        
          p
          t
        
      
      ⁢
      
        [
        
          
            
              dx
              2
            
            ⁡
            
              (
              
                p
                t
              
              )
            
          
          +
          
            
              dy
              2
            
            ⁡
            
              (
              
                p
                t
              
              )
            
          
        
        ]
      
    
  




in which dx(•) and dy(•) are the gradients in x-direction and y-direction in the image respectively. If bt>1.3 min{bt−1, bt+1}, it will be considered that the current frame is a blurred image, and MLHt will be set to be invalid.
Step S106: Image Completion
Because the high-spatial-resolution image may not overlap the whole outputted image regions, to complete the outputted image, the invisible regions in the high-spatial-resolution image may need to be inpainted. According to an embodiment of the present invention, four strategies are adopted in turn to inpaint the output image so that historic high-spatial-resolution information may be utilized maximally. Finally, the image is post processed to ensure continuity of image intensity and spatial continuity. The specific steps thereof are as follows.
Step S301: Strategy 1 is used for inpainting, i.e., inpainting by the high-spatial-resolution image. If MLHt is valid, IHt may be transformed to Ioutt via MLHt, and the overlapped region can be inpainted by IHt.
Step S302: The high-spatial-resolution background image is estimated to update the high-spatial-resolution background model IHBt. Hereinbefore, the foreground image and background image segmenting have been segmented for the low-spatial-resolution image ILt. If MLHt of the current image is valid, the background region in the current high-spatial-resolution image IHt may be obtained via the background region ILBt in ILt. The background model IHBt is updated by the high-spatial-resolution regions in the 1th 2th , . . . , (t+50)th frames. For the background model IHBt+1 of the next frame, if MLHt is valid, the background region of the IHt+51 will be mapped onto IHBt, and the overlapped region will be updated with an attenuation factor 0.5, that is to say, the pixel intensity of the background region may be processed as follows: IHBt+1=0.5IHBt+0.5IHt+51, otherwise, IHBt+1=IHBt.
Step S303: Strategy 2 is used for inpainting, i.e., inpainting by the high-spatial-resolution background image. If the unfilled region of the output image contains background pixels, IHBt may be used for inpainting.
Step S304: Reference sample patch (SP) may be selected, and relative motion between the reference sample patch and the target frame may be calculated. And the key points of the step S304 lie in that:
1) The constructing and updating of the reference sample set;
2) How to select the optimal reference sample for the frame to be inpainted; and
3) How to calculate the relative motion field under high-spatial resolution while taking the temporal and spatial continuities into consideration.
The frame to be inpainted means that, exemplified by the tth frame, if it satisfies one of the following conditions, the foreground image may need to be inpainted by this frame using this strategy:
a) MLHt is invalid;
b) IHt does not contain all of the interested targets; and
c) IHt is a blurred image, which may be determined by the relative blurriness bt as described hereinabove.
Step S401: Generating and Updating Reference Frame Sequence
The reference sample patch may comprise a pair of image blocks containing foreground regions respectively, i.e. and ILt and IHt with only the foreground regions remained, which may be designated by SPt={SPLt, SPHt}, and the reference frame sequence is updated by FIFO (FIRST-IN-FIRST-OUT) strategy. According to an embodiment of the present invention, the sequence may have a maximum length of 60. When the current tth frame satisfies the following three conditions, the frame may generate a reference sample:
a) MLHt is valid;
b) IHt contains all of the interested targets; and
c) IHt is not a blurred image
Step S402: Selecting the reference frame SPreft related to the current frame
For the tth frame, only the region in ILt containing the entire target is considered which is denoted as sub(ILt). And similarities of all the SPLi(i=1, 2, . . . , 60) with the sub(ILt) are calculated, and the similarity may be determined by the following method.
Firstly, a translation model (dx, dy)T from SPLi to sub(ILt) is calculated. The initial value is selected as the difference between the center point coordinates of the foreground target in sub(ILt) and center point coordinates of the foreground target in the SPLi. Then, Newton iterating algorithm based on Hessian matrix is used for obtaining the translation model (dx, dy)T.
Then, similarity may be calculated based on the following formula:




  exp
  ⁡
  
    (
    
      
        
          
            
              -
              
                1
                
                  Num
                  ⁡
                  
                    (
                    p
                    )
                  
                
              
            
            ⁢
            
              ∑
              
                
                  p
                  ∈
                  
                    Foreg
                    ⁡
                    
                      (
                      
                        I
                        L
                        t
                      
                      )
                    
                  
                
                ,
                
                  
                    p
                    -
                    
                      
                        (
                        
                          dx
                          ,
                          dy
                        
                        )
                      
                      T
                    
                  
                  ∈
                  
                    Foreg
                    ⁡
                    
                      (
                      
                        SP
                        L
                        i
                      
                      )
                    
                  
                
              
            
          
        
      
      
        
          
            
            
              
                
                  (
                  
                    sub
                    ⁡
                    
                      (
                      
                        I
                        L
                        t
                      
                      )
                    
                  
                  )
                
                ⁢
                
                  (
                  p
                  )
                
              
              -
              
                
                  SP
                  L
                  i
                
                ⁡
                
                  (
                  
                    p
                    -
                    
                      
                        (
                        
                          dx
                          ,
                          dy
                        
                        )
                      
                      T
                    
                  
                  )
                
              
            
            
          
        
      
    
    )
  




in which Foreg(SPLi) is the pixel sets of the foreground target in SPLi, Foreg(ILt) is the pixel sets of the foreground target in ILt, p is a pixel in the intersection of the foreground pixel set of SPLi after translation transformation and the Foreg(ILt), Num(p) is the pixel number in the intersection. If the pixel number in the intersection is less than 60% of the pixels in the Foreg(SPLi) or less than 60% of the pixels in Foreg(ILt), the similarity will be zero.
If the current frame is valid, the current frame will be a related reference frame which is denoted as SPreft, i.e. reft=t, otherwise, the reference frame having a minimal similarity with the sub(ILt) is used as the related reference frame. If the maximal similarity is less than a similarity threshold ThMAD=exp(−20), it will be considered that there is no related reference frame with sub(ILt) i.e., the SPreft is invalid, otherwise, SPreft is valid.
Step S403: Inter-frame high-spatial-resolution image optical flow field VH between the current frame and the reference frame is estimated.
The step of estimating the VH via SPHrefi(i=t−1t,t+1) may be as follows.
First step: The SPHreft−1, SPHreft, SPHreft+1 are adjusted to SPt−1t, SPtt, SPt+1t respectively to enhance comparability on a more uniform scale, since refi, (i=t−1,t,t+1) are sampled at different time with possible scale and translation variation. If they are pre-adjusted, the comparability therebetween may be increased to reduce the introduced errors in the following optical flow estimation. Affine transformation may be presumably adopted in the present invention with only scale transformation and translation transformation considered. Because the tth frame is the target to be processed, the center and dimension of the target in ILt may be used as a reference.
Second step: The high-spatial-resolution optical flow field VH is estimated by the adjusted SPt−1t, SPtt, SPt+1t.
The optical flow field is used because there are differences on the image contents although these reference frames may have been adjusted, and the differences thereof may not be described easily and accurately via models such as affine model or projection model. And pyramidal Lucas-Kanade optical flow method is used for estimating the optical flow fields Vt,t−1H and Vt,t+1H from SPtt to SPt−1tand SPt+1t respectively. Based on the assumption of inter-frame continuity, it may be considered that the inter-frame optical flow satisfies the linear model approximately. That is to say, under an ideal condition, the optical flow at (x, y) in SPtt satisfies Vt,t−1H(x, y)=−Vt,t+1H(x, y). Therefore, the optical flow filed VH from SPtt to the target frame ItH may be approximated by




  
    
      1
      2
    
    ⁢
    
      (
      
        
          V
          
            t
            ,
            
              t
              -
              1
            
          
          H
        
        +
        
          V
          
            t
            ,
            
              t
              +
              1
            
          
          H
        
      
      )
    
  
  ,




i.e.:





  
    V
    H
  
  =
  
    
      1
      2
    
    ⁢
    
      (
      
        
          V
          
            t
            ,
            
              t
              -
              1
            
          
          H
        
        +
        
          V
          
            t
            ,
            
              t
              +
              1
            
          
          H
        
      
      )
    
  




Step S404: Inter-frame low-spatial-resolution image optical flow field VL is estimated.
Optical flow field from sub(ILreft) adjusted by Mref(t)ST, designated by sub(ILreft), to tar(ILt) is considered. Although VH comprises the temporal and spatial continuity assumption, there may be a great local difference between SPtt and the target frame ItH, the assumption of Vt,t−1H(x, y)=−Vt,t+1H(x, y) may not be satisfied which may lead to invalidity of VH(x, y) or singularity. In addition, if SPt−1 and SPt+1 are invalid, VH is invalid at this time. Therefore, it is necessary to construct a corresponding relationship between SPtt and the target frame ItH. Because ItH is an unknown target image, it may only be obtained by the low-spatial-resolution image sub(ILref)adj corresponding thereto and tar(ILt). Because it may be considered to be smooth locally between sub(ILreft)adj and tar(ILt), although the optical flow field is obtained under a low scale, it may still reflect the approximate local differences after it is magnified to the scale of IjH.
According to an embodiment of the present invention, the pyramidal Lucas-Kanade method is used for estimating the optical flow field FLt between sub(ILreft)adj and tar(ILt). According to an embodiment of the present invention, VL=5FLt.
The flow chart for calculating VH and VL is shown in FIG. 3.
Step S405: Optical flow field FHt between SPHreft and Ioutt is estimated.
If SPreft is valid, the optical flow field FHt will be estimated. The calculation of the optical flow field from SPtt to the target frame or the output frame Ioutt is mainly through the following three steps.
a) The high-spatial-resolution optical flow field VH is obtained using the adjusted SPt−1t, SPtt and SPt+1t, which reflects temporal continuity;
b) The low-spatial-resolution optical flow field VL is obtained using sub(ILreft)adj and tar(ILt), which reflects accuracy of the spatial position, especially in the case of VH being invalid when the neighboring frames have no corresponding SPs;
c) optical flow field smoothing is performed for the VH and VL with the singular optical flow value removed. It should be noted that if VH and VL are obtained with the smoothness considered, this step may be omitted.
FHt may be solved by the following formula:




  
    min
    ⁢
    
      
    
    ⁢
    E
  
  =
  
    
      β
      ⁢
      
        
          ∑
          
            
              (
              
                x
                ,
                y
              
              )
            
            ∈
            
              
            
            ⁢
            V
          
        
        ⁢
        
          
            
              ω
              1
            
            ⁡
            
              (
              
                x
                ,
                y
              
              )
            
          
          ⁡
          
            [
            
              
                
                  (
                  
                    u
                    -
                    
                      u
                      H
                    
                  
                  )
                
                2
              
              +
              
                
                  (
                  
                    v
                    -
                    
                      v
                      H
                    
                  
                  )
                
                2
              
            
            ]
          
        
      
    
    +
    
      γ
      ⁢
      
        
          ∑
          
            
              (
              
                x
                ,
                y
              
              )
            
            ∈
            
              
            
            ⁢
            V
          
        
        ⁢
        
          
            
              ω
              2
            
            ⁡
            
              (
              
                x
                ,
                y
              
              )
            
          
          ⁡
          
            [
            
              
                
                  (
                  
                    u
                    -
                    
                      u
                      L
                    
                  
                  )
                
                2
              
              +
              
                
                  (
                  
                    v
                    -
                    
                      v
                      L
                    
                  
                  )
                
                2
              
            
            ]
          
        
      
    
  




in which V is the valid region in the image, (x, y) denotes a pixel in V, u and v are the abbreviations of u(x, y) and v(x, y) representing the components in x direction and y direction of FHt at the point of (x, y) respectively. (uH, vH) represents the value of VH at (x, y), ω1(x, y) represents a weight. According to an embodiment of the present invention, ω1(x, y)=exp(−∥(uH, vH)∥/10). (uL, vL) represents the value of VL at (x, y), ω2(x, y) is a weight, and according to an embodiment of the present invention, ω2(x, y)=1. β and γ are scale ratios. When the neighboring frames are valid, β becomes larger which means that the neighboring information weight becomes larger with the temporal and spatial continuities dominated; if the neighboring frames are invalid, spatial accuracy should be reconsidered, and at this time, γ is larger. In the present invention, if the neighboring reference frames are valid, β=2γ; otherwise β=0.
Step S305: Next, strategy 3 is used for completion, i.e. foreground reference inpainting. More specifically, the foreground region of the output image Ioutt may be inpainted via bilinear interpolation after SPHreft is transformed by the optical flow field FHt.
Step S306: Finally, strategy 4 is used for completion, i.e. low-spatial-resolution image inpainting for the image region still not inpainted. That is to say, the remaining region which is not inpainted is inpainted by the low-spatial-resolution image ILt via bilinear interpolation.
Step S107: Post Processing. Post processing is applied to adjust the intensities after Ioutt inpainting. This is necessary because even when all pixels in Ioutt are perfectly inpainted, the intensity might still be inconsistent in the following two aspects of: 1) spatial inconsistence near the junction among neighboring regions with different inpainting types; 2) temporal inconsistence between successive frames. These phenomena might affect the visual effect sometimes.
The post processing may comprise the steps as follows.
1. The intensities are adjusted.
The intensities of R1 and R4 are adjusted to be consistent with R2, and the adjusting method may be the one similar to that described in S202. R1 represents the image region of the output image Ioutt inpainted by the step S302. R2 represents the image region of the output image Ioutt inpainted by the step S303. R3 represents the image region of the output image Ioutt inpainted by the step S305. R4 represents the image region of the output image Ioutt inpainted by the step S306. When R1 is adjusted, the pixels overlapping with the IHBt may be used for calculating an intensity mapping model with only the overlapped pixels adjusted. When R4 is adjusted, the pixels overlapping with the IHBt may be used for calculating an intensity mapping model with all the pixels adjusted.
2. Spatial continuity is adjusted.
For the regions R1, R2 and R4 of the output image Ioutt, they may be processed as follows: a structuring element such as 5×5 is used for dilating transition regions, i.e. boundaries, and the boundaries after dilation is smoothened with a 3×3 mean filter with the region R3 unchanged.
In order to better understand the embodiments of the present invention, the above method of the present invention will be described in more detail hereinafter.
Step 1). A first active camera with a variable view angle or resolution is used as a static camera for monitoring a panoramic image, and a second active camera with a variable view angle or resolution is used for capturing an interested moving target.
Step 2). A panoramic image, which is also referred to as a low-spatial-resolution image ILt, is input into a PC from the first active camera, to convert each frame into a first gray image; an image recording the moving target, which is also referred to as a high-spatial-resolution image IHt, is input into the PC from the second active camera, to convert each frame into a second gray image; and the low-spatial-resolution and high-spatial-resolution images ILt, IHt are replaced by the first and second gray images respectively.
Step 3). The registration of the low-spatial-resolution and high-spatial-resolution images are performed accordingly. A target region IL—tart including the moving target in the low-spatial-resolution image ILt is determined to obtain an output image Ioutt in the high-spatial-resolution image IHt corresponding to the target region IL—tart, then a mapping model MLHt between the low-spatial-resolution image ILt and the high-spatial-resolution image IHt is calculated. In particular, this step may further comprise the following.
Step 3.1). A target region IL—tart is determined or selected. In particular, step 3.1) may further comprise the following.
Step 3.1.1). A low-spatial-resolution background ILBt is generated for the low-spatial-resolution image ILt. An updating formula at pixel (x, y) is ILBt(x, y)=(1−α)ILBt−1(x, y)+αILt(x, y),
in which an updating coefficient α=0.05; an initial low-spatial-resolution background model ILBt=0(x, y)=ILt=0(x, y); and if |ILt(x, y)−ILBt(x, y)|>TLB, TLB=20, ILt at pixel (x, y) belongs to a foreground region, otherwise, it belongs to a background region.
Step 3.1.2). By using a Mean-shift tracking algorithm provided by Opencv, and proving a low-spatial-resolution image ILt at the tth frame and the foreground region obtained in step 3.1.1), the position of the interested target in the image ILt may be obtained, and the mean value smoothing to the centers of the tracked target within a predetermined neighboring frames may be performed. The smoothed center of the tracked target is the center of the target, and is also the center of the rectangular region, and the rectangular region may have a length and a width with predetermined pixels respectively. According to an embodiment of the invention, the length and width thereof may be set to 64×48 pixels respectively. The size of the final high-spatial-resolution output image Ioutt may be ko times as large as that of the target region IL—tart in the low-spatial-resolution image. According to an embodiment of the invention, ko=5.
Step 3.2). A rough registration model MLH1t between the low-spatial-resolution image ILt and the high-spatial-resolution image IHt is generated by a feature-based registration method. According to an embodiment of the invention, in particular, the step 3.2) may comprise the following.
Step 3.2.1). SURF or SIFT feature points of the target region IL—tart in the low-spatial-resolution image ILt and the high-spatial-resolution image IHt at the tth frame may be calculated respectively.
Step 3.2.2). A distance between each SURF or SIFT feature point in the high-spatial-resolution image IHt and each SURF or SIFT feature point of the target region IL—tart in the low-spatial-resolution image ILt is calculated, s12=∥v1−v2∥, in which v1 and v2 represent SIFT characteristic vectors corresponding to two SIFT feature points respectively. Then two groups of results smin1 and smin2 with the smallest distance are considered. If smin1<Ts□smin2, Ts=0.7, the SURF/SIFT feature point corresponding to smin1 in will be the matched SURF/SIFT feature point between the high-spatial-resolution image IHt and the target region IL—tart in the low-spatial-resolution image ILt, otherwise, the point may be considered to have no matching feature points. If the number of the total matched feature pixels between the two images is less than 10, the mapping model MLHt between ILt and IHt will be invalid, and step (4) will follow, otherwise, step (3.3) will follow.
Step 3.2.3). An affine transformation matrix




  
    M
    
      LH
      ⁢
      
        
      
      ⁢
      1
    
    t
  
  =
  
    [
    
      
        
          
            m
            1
          
        
        
          
            m
            2
          
        
        
          
            m
            3
          
        
      
      
        
          
            m
            4
          
        
        
          
            m
            5
          
        
        
          
            m
            6
          
        
      
      
        
          0
        
        
          0
        
        
          1
        
      
    
    ]
  




based on the matched SIFT feature point pairs {(xi1, yi1)εILt, (xi2, yi2)εIHt}, i=1, 2, . . . , n between the high-spatial-resolution image IHt and the target region IL—tart the low-spatial-resolution image ILt is generated, in which the parameters are generated by the following formula:




  
    
      
        
          [
          
            
              m
              1
            
            ,
            
              m
              2
            
            ,
            
              m
              3
            
            ,
            
              m
              4
            
            ,
            
              m
              5
            
            ,
            
              m
              6
            
          
          ]
        
        T
      
      =
      
        
          
            (
            
              
                A
                T
              
              ⁢
              A
            
            )
          
          
            -
            1
          
        
        ⁢
        AX
      
    
    ,
    
      in
      ⁢
      
        
      
      ⁢
      which
    
  




  
    
      A
      =
      
        [
        
          
            
              
                x
                1
                1
              
            
            
              
                y
                1
                1
              
            
            
              1
            
            
              0
            
            
              0
            
            
              0
            
          
          
            
              0
            
            
              0
            
            
              0
            
            
              
                x
                1
                1
              
            
            
              
                y
                1
                1
              
            
            
              1
            
          
          
            
              
                x
                2
                1
              
            
            
              
                y
                2
                1
              
            
            
              1
            
            
              0
            
            
              0
            
            
              0
            
          
          
            
              0
            
            
              0
            
            
              0
            
            
              
                x
                2
                1
              
            
            
              
                y
                2
                1
              
            
            
              1
            
          
          
            
              ⋮
            
            
              ⋮
            
            
              ⋮
            
            
              ⋮
            
            
              ⋮
            
            
              ⋮
            
          
          
            
              
                x
                n
                1
              
            
            
              
                x
                n
                1
              
            
            
              1
            
            
              0
            
            
              0
            
            
              0
            
          
          
            
              0
            
            
              0
            
            
              0
            
            
              
                x
                1
                1
              
            
            
              
                x
                1
                1
              
            
            
              1
            
          
        
        ]
      
    
    ,
    
      
    
    ⁢
    
      X
      =
      
        [
        
          
            
              
                x
                1
                2
              
            
          
          
            
              
                y
                1
                2
              
            
          
          
            
              
                x
                2
                2
              
            
          
          
            
              
                y
                2
                2
              
            
          
          
            
              ⋮
            
          
          
            
              
                x
                n
                2
              
            
          
          
            
              
                y
                n
                2
              
            
          
        
        ]
      
    
  



Step 3.3). An adjusted image IL—adjt is generated by adjusting an intensity of the low-spatial-resolution image ILt. In particular, this step may comprise the following.
Step 3.3.1). An intensity mapping region is selected. The matched SIFT feature point pairs in the two images IHt and IL—tart in step 3.2) are respectively represented by a convex polygon, with the vertex of the polygon represented by the feature points. All the feature point is positioned inside or on the vertex of the polygon, and the inside portion of the convex polygon is the intensity mapping region.
Step 3.3.2). Cumulative intensity histograms of the high-spatial-resolution image IHt and the low-spatial-resolution image ILt are obtained by accumulating the intensity histograms hist(k), k=0, 1, . . . , 255 in the convex polygon according to the following formula:




  
    
      Accu
      ⁡
      
        (
        K
        )
      
    
    =
    
      
        
          ∑
          
            k
            =
            1
          
          K
        
        ⁢
        
          hist
          ⁡
          
            (
            k
            )
          
        
      
      
        
          ∑
          
            k
            =
            1
          
          32
        
        ⁢
        
          hist
          ⁡
          
            (
            k
            )
          
        
      
    
  
  ,
  
    K
    =
    0
  
  ,
  1
  ,
  ...
  ⁢
  
    
  
  ,
  255.




Step 3.3.3). The cumulative intensity histograms of ILt and IHt are represented by Accu1 and Accu2 respectively, and three intensity sets G1, G2, G3 are defined according to the following:

G1={K:0≦Accu1(K)<0.05}

G2={K:0.05≦Accu1(K)<0.95}

G3={K:0.95≦Accu1(K)≦1}

The mapping model is selected as a three-piece-wise linear model




  
    K
    2
  
  =
  
    
      MI
      ⁡
      
        (
        
          K
          1
        
        )
      
    
    =
    
      {
      
        
          
            
              
                
                  
                    a
                    1
                  
                  ⁢
                  
                    K
                    1
                  
                
                +
                
                  b
                  1
                
              
              ,
              
                
                  K
                  1
                
                ∈
                
                  G
                  1
                
              
            
          
        
        
          
            
              
                
                  
                    a
                    2
                  
                  ⁢
                  
                    K
                    1
                  
                
                +
                
                  b
                  2
                
              
              ,
              
                
                  K
                  1
                
                ∈
                
                  G
                  2
                
              
            
          
        
        
          
            
              
                
                  
                    a
                    3
                  
                  ⁢
                  
                    K
                    1
                  
                
                +
                
                  b
                  3
                
              
              ,
              
                
                  K
                  1
                
                ∈
                
                  G
                  3
                
              
              ,
            
          
        
      
    
  




in which K1 and K2 represent the intensities of ILt and IHt respectively. When using the following objective function to linearly fit KεG2, an intensity mapping model between the high-spatial-resolution image IHt and the low-spatial-resolution image ILt is K2=MI(K1)=a2K1+b2, K1εG2:





  
    min
    
      MI
      ⁡
      
        (
        •
        )
      
    
  
  ⁢
  
    
      ∑
      
        K
        ∈
        
          G
          2
        
      
    
    ⁢
    
      
      
        
          
            Accu
            1
          
          ⁡
          
            (
            K
            )
          
        
        -
        
          
            Accu
            2
          
          ⁡
          
            (
            
              MI
              ⁡
              
                (
                K
                )
              
            
            )
          
        
      
      
    
  




The intensity sets G1 and G3 are used to fit the models K2=MI(K1)=a1K1+b1, K1εG1 and K2=MI(K1)=a3K1+b3, K1εG3 respectively, so that, MI(0)=0, and MI(255)=255.
Step 3.3.4). The intensity of the low-spatial-resolution image ILt is adjusted based on the intensity mapping model MI(k) to generate the adjusted image IL—adjt.
Step 3.4). A refined registration model MLH2t between the low-spatial-resolution image ILt and the high-spatial-resolution image IHt is generated using a direct pixel-based registration method. In particular, this step may comprise the following.
Step 3.4.1). The high-spatial-resolution image IHt is transformed based on the rough registration model MLH1t in step 3.2) to generate an image IH—adjt. The transforming method may be as follows:
the value of the image IH—adjt at coordinate point (xi, yi) is IH—adjt(xi, yi)=IHt(f(xi, yi, (MLH1t)−1)), in which f is a homogeneous coordinates conversion function, and may be calculated as follows:
f(x, y, M)=(x′, y′), in which x′ and y′ is obtained by the following formula: [x′, y′, 1]T=M[x, y, 1]T.
Step 3.4.2). A gradient based Hessian matrix is utilized to iteratively solve the following optimal problem to generate a model Mrefinedt 




  
    
      M
      refined
      t
    
    =
    
      arg
      ⁢
      
        
      
      ⁢
      
        
          min
          M
        
        ⁢
        
          
            ∑
            i
          
          ⁢
          
            
            
              
                
                  I
                  H_adj
                  t
                
                ⁡
                
                  (
                  
                    
                      x
                      i
                    
                    ,
                    
                      y
                      i
                    
                  
                  )
                
              
              -
              
                
                  I
                  L_adj
                  t
                
                ⁡
                
                  (
                  
                    f
                    ⁡
                    
                      (
                      
                        
                          x
                          i
                        
                        ,
                        
                          y
                          i
                        
                        ,
                        M
                      
                      )
                    
                  
                  )
                
              
            
            
          
        
      
    
  
  ,




in which (xi, yi) is the image of IHt transformed by MLH1t, f refers to a homogeneous coordinates conversion function in step 3.4.1); and according to an embodiment of the invention, an initial value M0=I3×3.
Step 3.4.3). If Mrefinedt generated in step 3.4.2) satisfies either of the following two conditions, Mrefinedt and MLH2t will be invalid, will not be calculated, and step 4) will follow:

    
    
        a) ∥R2×2M−I2×2∥∞<0.3;
        b) ∥t2×1M∥∞<4;
        in which Mrefinedt=[RM tM], [R2×2M t2×1M] is the first two rows of Mrefinedt.
    
    


Step 3.4.4). The refined registration model MLHt is generated based on the rough registration model MLH1t and the registration model Mrefinedt, where MLHt=MLH1tMrefinedt.
Step 3.5). The output image Ioutt is smoothed based on the 2N+1 neighboring frames. According to an embodiment of the invention, N=5. In particular, this step may comprise the following.
Step 3.5.1). A transforming model Mji from the jth high-spatial-resolution image to the ith high-spatial-resolution image is generated.
The foreground target in IHt is obtained via the refined registration model MLH2t by the corresponding foreground region in ILt generated in step 3.1.1) using the transforming method in step 3.4.1), thus obtaining the background region of IHt. By the method in step 3.2), the transforming model Mji from the jth high-spatial-resolution image to the ith high-spatial-resolution image is generated.
Step 3.5.2). The smoothing model MLHt may be computed by the following formula:




  
    M
    LH
    t
  
  =
  
    
      
        ∑
        
          i
          =
          
            t
            -
            N
          
        
        
          t
          +
          N
        
      
      ⁢
      
        
          ω
          i
        
        ⁢
        
          δ
          i
        
        ⁢
        
          M
          i
          t
        
        ⁢
        
          M
          
            LH
            ⁢
            
              
            
            ⁢
            2
          
          i
        
      
    
    
      
        ∑
        
          i
          =
          
            t
            -
            N
          
        
        
          t
          +
          N
        
      
      ⁢
      
        
          ω
          i
        
        ⁢
        
          δ
          i
        
      
    
  




in which ωi is Gaussian weight N=5,




  
    
      ω
      i
    
    =
    
      
        1
        
          
            
              2
              ⁢
              π
            
          
          ⁢
          σ
        
      
      ⁢
      
        e
        
          
            
              (
              
                i
                -
                t
              
              )
            
            2
          
          
            2
            ⁢
            
              σ
              2
            
          
        
      
    
  
  ,




σ=1.5, δi is the characteristic function satisfying:





  
    δ
    j
  
  =
  
    {
    
      
        
          
            1
            ,
            
              if
              ⁢
              
                
              
              ⁢
              
                M
                j
                i
              
              ⁢
              
                
              
              ⁢
              and
              ⁢
              
                
              
              ⁢
              
                M
                
                  LH
                  ⁢
                  
                    
                  
                  ⁢
                  2
                
                i
              
              ⁢
              
                
              
              ⁢
              are
              ⁢
              
                
              
              ⁢
              both
              ⁢
              
                
              
              ⁢
              valid
            
          
        
      
      
        
          
            0
            ,
            otherwise
          
        
      
    
  




Step 3.5.3). The relative blurriness of the current frame bt is computed by the following formula:




  
    
      b
      t
    
    =
    
      1
      
        
          ∑
          
            p
            t
          
        
        ⁢
        
          [
          
            
              
                dx
                2
              
              ⁡
              
                (
                
                  p
                  t
                
                )
              
            
            +
            
              
                dy
                2
              
              ⁡
              
                (
                
                  p
                  t
                
                )
              
            
          
          ]
        
      
    
  
  ,




in which pt is the pixel point in the high-spatial-resolution image at the tth frame, dx(•) and dy(•) are gradients in x-direction and y-direction respectively.
If bt>1.3 min{bt−1, bt+1}, the current frame will be a blurred image, and MLHt will be invalid.
Step 4). The image is completed. The output image Ioutt is inpainted based on the registration model MLHt and the high-spatial-resolution image IHt to complete the output image Ioutt. In particular, this step may comprise the following.
Step 4.1). The high-spatial-resolution background image IHBt corresponding to ILBt at the tth frame is estimated. In particular, this step may comprise the following.
Step 4.1.1). If the registration model MLHt in step 4.1.1) is valid, the background region in the high-spatial-resolution image IHt corresponding to the background region ILBt in the low-spatial-resolution image ILt of step 3.1.1) will be obtained by the transforming method in step 3.4.1) via the transforming model MLHt.
Step 4.1.2). For the tth frame, the high-spatial-resolution background regions in the 1st, 2nd . . . (t+50)th frames, are used to update the current high-spatial-resolution background model IHBt For the background model IHBt+1 of the next frame, if MLHt is valid, the background region of IHt+51 will be mapped onto IHBt, and the overlapping region will be updated with an attenuation factor 0.5, that is to say, the pixel intensity of the background region will be processed as follows: IHBt+1=0.5IHBt+0.5IHt+51; otherwise, IHBt+1=IHBt.
Step 4.2). Ioutt is inpainted. In particular, this step may comprise the following.
Step 4.2.1). The output image Ioutt is inpainted based on the registration model MLHt and the high-spatial-resolution image IHt to complete the output image Ioutt which is not fully covered by the high-spatial-resolution image IHt. If the transforming model MLHt is valid, the background region in the high-spatial-resolution image IHt will be transformed onto the output image Ioutt according to the transforming method in step 3.4.1) via the transforming model MLHt, and the overlapping region in the output image Ioutt will be inpainted by the intensity of the high-spatial-resolution image IHt.
Step 4.2.2). For the background region, if the remaining region of the output image Ioutt contains background pixels corresponding to valid pixels in the background image IHBt will be used directly for inpainting the output image Ioutt.
Step 4.2.3). For the foreground region, if the tth frame meets one of the following three conditions, step 4.2.3.1) will follow. Otherwise, step 4.2.4) will be skipped to:

    
    
        a) the transforming model MLHt being invalid;
        b) the high-spatial-resolution image IHt not containing a complete interested object;
        c) the high-spatial-resolution image IHt in step 3.5.3) being a blurred image.
    
    


Step 4.2.3.1). A reference sample sequence is established and updated.
According to an embodiment of the invention, the reference sample sequence has a maximum length of 60. If the tth frame meets one of the following three conditions, the frame will generate a reference sample:

    
    
        a) the transforming model MLHt being valid;
        b) the high-spatial-resolution image IHt containing a complete interested object;
        c) the high-spatial-resolution image IHt in step 3.5.3) being not a blurred image.
    
    


An SP consists of a pair of image blocks SPLi and SPHi both of which contain a foreground region respectively, that is to say, SPt={SPLt, SPHt}, in which SPLi represents a low-spatial-resolution fixed reference frame (40×40) containing only the foreground target in the ith frame, and SPHi represents a high-spatial-resolution reference frame (200×200) corresponding to SPLi containing only the foreground target in the ith frame. The reference sample sequence is updated by a First-In-First-Out (FIFO) strategy.
Step 4.2.3.2). A most matching reference frame SPreft with the current frame is estimated in the reference sample sequence.
For the tth frame, only rectangular image region sub(ILt) containing the complete target in ILt is considered, and similarities of all the SPLi, i=1, 2, . . . , 60 in the reference frame sequence with the sub(ILt) calculated as follows.
Step 4.2.3.2.1). A translation model (dx, dy)T from SPLi to sub(ILt) is calculated. The initial value is selected as the difference between the center point coordinates of the foreground target in sub(ILt) and center point coordinates of the foreground target in the SPLi. Then, iteration based gradient descent optimizing algorithm is used for obtaining the translation model (dx, dy)T.
Step 4.2.3.2.2. Similarity may be calculated based on the following formula:




  exp
  ⁡
  
    (
    
      
        -
        
          1
          
            Num
            ⁡
            
              (
              p
              )
            
          
        
      
      ⁢
      
        
      
      ⁢
      
        
          ∑
          
            
              p
              ∈
              
                Foreg
                ⁡
                
                  (
                  
                    I
                    L
                    t
                  
                  )
                
              
            
            ,
            
              
                p
                -
                
                  
                    (
                    
                      dx
                      ,
                      
                        
                      
                      ⁢
                      dy
                    
                    )
                  
                  T
                
              
              ∈
              
                Foreg
                ⁢
                
                  
                
                ⁢
                
                  (
                  
                    SP
                    L
                    i
                  
                  )
                
              
            
          
        
        ⁢
        
          
          
            
              
                (
                
                  sub
                  ⁡
                  
                    (
                    
                      I
                      L
                      t
                    
                    )
                  
                
                )
              
              ⁢
              
                (
                p
                )
              
            
            -
            
              
                SP
                L
                i
              
              ⁡
              
                (
                
                  p
                  -
                  
                    
                      (
                      
                        dx
                        ,
                        dy
                      
                      )
                    
                    T
                  
                
                )
              
            
          
          
        
      
    
    )
  




in which Foreg(SPLi) is the pixel sets of the foreground target in SPLi, Foreg(ILt) is the pixel sets of the foreground target in ILt, p is a pixel in the intersection of the foreground pixel set of SPLi after translation transformation and the Foreg(ILt), Num(p) is the pixel number in the intersection. If the pixel number in the intersection is less than 60% of the pixels in the Foreg(SPLi) or less than 60% of the pixels in Foreg(ILt), the similarity will be zero.
If the current tth frame is valid, the current frame will be a related reference frame which is denoted as SPreft, i.e. reft=t, otherwise, the reference frame having a minimal similarity with the sub(ILt) will be used as the related reference frame. If the maximal similarity is less than a similarity threshold ThMAD=exp(−20), it will be considered that there is no related reference frame with sub(ILt), i.e., the SPreft is invalid, otherwise, SPreft is valid.
Step 4.2.3.2.3). High-spatial-resolution image optical flow field VH between the current frame and the reference frame is estimated, which is estimated by three neighboring frames and the corresponding reference frames SPHrefi,i=t−1,t,t+1.
Step 4.2.3.2.3.1). A translation model from SPHreft−1 to SPHreft is obtained by the iteration based gradient descent optimizing algorithm. And SPHreft−1 is transformed to SPt−1t by the translation model, thus removing the entire motion between SPHreft−1 and SPt−1t. Similarly, SPHreft+1 is transformed to SPt+1t by the translation model, thus removing the entire motion between SPHreft+1 and SPt+1t. For the current frame, i.e., the tth frame, SPtt=SPHreft.
Step 4.2.3.2.3.2). The high-spatial-resolution optical flow field VH is estimated by the adjusted SPt−1t, SPtt, SPt+1t.
The pyramidal Lucas-Kanade optical flow method is used for estimating the optical flow fields Vt,t−1H and Vt,t+1H from SPtt to SPt−1t and SPt+1t respectively. The optical flow field VH from SPtt to the target frame ItH may be approximated by



  
    
      
        1
        2
      
      ⁢
      
        (
        
          
            V
            
              t
              ,
              
                t
                -
                1
              
            
            H
          
          +
          
            V
            
              t
              ,
              
                t
                +
                1
              
            
            H
          
        
        )
      
    
    ,
    
      i
      .
      e
      .
      
        :
      
    
  




  5




  
    
      V
      H
    
    =
    
      
        1
        2
      
      ⁢
      
        (
        
          
            V
            
              t
              ,
              
                t
                -
                1
              
            
            H
          
          +
          
            V
            
              t
              ,
              
                t
                +
                1
              
            
            H
          
        
        )
      
    
  



Step 4.2.3.2.4). Inter-frame low-spatial-resolution image optical flow field VL is estimated. According to an embodiment of the present invention, the pyramidal Lucas-Kanade method is also used for estimating the optical flow field FLt between sub(ILreft)adj and tar(ILt). According to an embodiment of the present invention, VL=5FLt.
Step 4.2.3.2.5). If SPreft is valid, the optical flow field FHt between SPHreft and Ioutt will be estimated. FHt may be solved by the following formula:




  
    
      min
      ⁢
      
        
      
      ⁢
      E
    
    =
    
      
        β
        ⁢
        
          
        
        ⁢
        
          
            ∑
            
              
                (
                
                  x
                  ,
                  y
                
                )
              
              ∈
              
                
              
              ⁢
              V
            
          
          ⁢
          
            
              
                ω
                1
              
              ⁡
              
                (
                
                  x
                  ,
                  y
                
                )
              
            
            ⁡
            
              [
              
                
                  
                    (
                    
                      u
                      -
                      
                        u
                        H
                      
                    
                    )
                  
                  2
                
                +
                
                  
                    (
                    
                      v
                      -
                      
                        v
                        H
                      
                    
                    )
                  
                  2
                
              
              ]
            
          
        
      
      +
      
        γ
        ⁢
        
          
            ∑
            
              
                (
                
                  x
                  ,
                  y
                
                )
              
              ∈
              V
            
          
          ⁢
          
            
              
                ω
                2
              
              ⁡
              
                (
                
                  x
                  ,
                  y
                
                )
              
            
            ⁡
            
              [
              
                
                  
                    (
                    
                      u
                      -
                      
                        u
                        L
                      
                    
                    )
                  
                  2
                
                +
                
                  
                    (
                    
                      v
                      -
                      
                        v
                        L
                      
                    
                    )
                  
                  2
                
              
              ]
            
          
        
      
    
  
  ,




in which V is the valid region in the image, (x, y) denotes a pixel in V, u and v are the abbreviations of u(x, y) and v(x, y) representing the components in x direction and y direction of FHt at the point of (x, y) respectively. (uH, vH) represents the value of VH at (x, y), ω1(x, y) represents a weight. According to an embodiment of the present invention, ω1(x, y)=exp(−∥(uH, vH)∥/10). (uL, vL) represents the value of VL at (x, y), ω2(x, y) is a weight, and according to an embodiment of the present invention, ω2(x, y)=1. β and γ are scale ratios. When the neighboring frames are valid, β becomes larger which means that the neighboring information weight becomes larger with the temporal and spatial continuities dominated; if the neighboring frames are invalid, spatial accuracy should be reconsidered, and at this time, γ is larger. In the present invention, if the neighboring reference frames are valid, β=2γ; otherwise β=0.
Step 4.2.3.2.6). Foreground reference is inpainted. More specifically, the foreground region of the output image Ioutt may be inpainted via bilinear interpolation after SPHreft is transformed by the optical flow field FHt.
Step 4.2.4). Low-spatial-resolution image is inpainted for the image region still not inpainted. That is to say, the remaining region which is not inpainted is inpainted by the low-spatial-resolution image ILt via bilinear interpolation.
Step 4.3). The intensities are adjusted.
The intensities of R1 and R4 are adjusted to be consistent with R2, and the adjusting method may be the one similar to that described in Step 3.3). R1 represents the image region of the output image Ioutt inpainted by the step 4.2.1). R2 represents the image region of the output image Ioutt inpainted by the step 4.2.2). R3 represents the image region of the output image Ioutt inpainted by the step 4.2.3). R4 represents the image region of the output image Ioutt inpainted by the step 4.2.4). When R1 is adjusted, the pixels overlapping with the IHBt may be used for calculating an intensity mapping model with only the overlapped pixels adjusted. When R4 is adjusted, the pixels overlapping with the IHBt may be used for calculating an intensity mapping model with all the pixels adjusted.
Step 4.4). Spatial continuity is adjusted.
Step 4.3). For the regions R1, R2 and R4 of the output image Ioutt, they may be processed as follows: a structuring element such as 5×5 is used for dilating transition regions, i.e. boundaries, and the boundaries after dilation is smoothened with a 3×3 mean filter with the region R3 unchanged.
According to the present invention, image registration problems between videos with different spatial resolutions may be solved smoothly. In addition, four following types of image completion strategies may be proposed: high-spatial-resolution image inpainting; high-spatial-resolution background image inpainting; foreground image inpainting and low-spatial-resolution image inpainting. Thus, current high-spatial-resolution information and historic high-spatial-resolution information may be fully used to inpaint target video. Through the above processing, the video may be used for collection of criminal evidences, storage of surveillance records, behavioral analysis of moving targets, etc. Experimental results have shown that the proposed stabilization and completion algorithms are very practical.
According to an embodiment of the invention, a video stabilizing system using a dual-camera system is further provided, comprising a first camera unit, a second camera unit and a controller, where the controller may be a PC or any other hardware device. The first camera unit is used for monitoring a panoramic image and sampling a low-spatial-resolution image ILt, where ILt represents the low-spatial-resolution image at the tth frame. The second camera unit is used for capturing a moving target and sampling a high-spatial-resolution image IHt where IHt represents the high-spatial-resolution image at the tth frame, with the second camera unit synchronous with the first camera unit. The controller is used for receiving the low-spatial-resolution image ILt and the high-spatial-resolution image IHt, outputting an output image Ioutt of the high-spatial-resolution image IHt corresponding to a target region IL—tart where the moving target is located in the low-spatial-resolution image ILt, generating a registration model MLHt between the low-spatial-resolution image ILt and the high-spatial-resolution image IHt, and inpainting the output image Ioutt based on the registration model MLHt and the high-spatial-resolution image IHt to complete the output image Ioutt which is not fully covered by the high-spatial-resolution image IHt. In an embodiment of the invention, the first and second camera units may be independently an active camera. In another embodiment of the invention, the first and second camera units may be a static camera and an active camera respectively.
In an embodiment of the invention, the controller comprises a receiving module, an output image selecting module, a registration model generating module and an image inpainting module. The receiving module is used for receiving the low-spatial-resolution image ILt and the high-spatial-resolution image IHt captured by the first camera unit and the second camera unit respectively. The output image selecting module is used for outputting the output image in the high-spatial-resolution image IHt corresponding to the target region IL—tart where the moving target is located in the low-spatial-resolution image ILt. The registration model generating module is used for generating a registration model MLHt between the low-spatial-resolution image ILt and the high-spatial-resolution image IHt. The image inpainting module is used for inpainting the output image Ioutt based on the registration model MLHt and the high-spatial-resolution image IHt to complete the output image Ioutt which is not fully covered by the high-spatial-resolution image IHt.
In an embodiment of the invention, the registration model generating module comprises a rough registration module generating sub-module, an adjusted image generating sub-module, and a refined registration module generating sub-module. The rough registration module generating sub-module is used for generating a rough registration model MLH1t between the low-spatial-resolution image ILt and the high-spatial-resolution image IHt using a feature-based alignment method. The correcting image generating sub-module is used for adjusting an intensity of the low-spatial-resolution image ILt to obtain the adjusted image IL—adjt. The refined registration module generating sub-module is used for generating a refined registration module MLH2t between the low-spatial-resolution image ILt and high-spatial-resolution image IHt using a pixel-based direct-alignment method based on the rough registration model MLH1t and the adjusted image IL—adjt.
In an embodiment of the invention, the output image is inpainted by the image inpainting module based on the strategies 1 to 4 as described above to obtain inpainted regions R1 to R4 respectively.
In an embodiment of the invention, the controller further comprises a post-processing module for post processing the output image after inpainting to adjust intensity and spatial continuity of the output image.
By using the video stabilizing system using a dual-camera system according to the present invention, image registration problems between videos with different spatial resolutions may be solved smoothly. In addition, four following types of image completion strategies may be proposed: high-spatial-resolution image inpainting; high-spatial-resolution background image inpainting; foreground image inpainting and low-spatial-resolution image inpainting. Thus, current high-spatial-resolution information and historic high-spatial-resolution information may be fully used to inpaint target video. Through the above processing, the video may be used for collection of criminal evidences, storage of surveillance records, behavioral analysis of moving targets, etc. Experimental results have shown that the proposed stabilization and completion algorithms are very practical.
Reference throughout this specification to “certain embodiments,” “one or more embodiments” or “an embodiment” means that a particular feature, structure, material, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Thus, the appearances of the phrases such as “in one or more embodiments,” “in certain embodiments,” “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily referring to the same embodiment of the invention. Furthermore, the particular features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments.
Although explanatory embodiments have been shown and described, it would be appreciated by those skilled in the art that changes, alternatives, and modifications can be made in the embodiments without departing from spirit and principles of the invention. Such changes, alternatives, and modifications all fall into the scope of the claims and their equivalents.

## Claims

1. A video stabilizing method, comprising the steps of:
1) capturing a low-spatial-resolution image ILt by a first camera for monitoring a panoramic area and a high-spatial-resolution image IHt by a second camera which is synchronous with the first camera for capturing an image of a moving target where ILt , IHt represent the low-spatial-resolution image and the high-spatial-resolution image at the tth time respectively;
2) determining a target region IL—tart including the moving target in the low-spatial-resolution image ILt , and obtaining an output image Ioutt of the high-spatial-resolution image IHt corresponding to the target region IL—tart;
3) generating a registration model MLHt between the low-spatial-resolution image ILt and the high-spatial-resolution image IHt ; and
4) inpainting the output image Ioutt based on the registration model MLHt and the high-spatial-resolution image IHt to complete the output image Ioutt .
wherein the step 3) further comprises:
generating a rough registration model MLH1 t between the low-spatial-resolution
image ILt and the high-spatial-resolution image IHt by employing a feature-based alignment algorithm;
generating an adjusted image It—adjt by adjusting an intensity of the low-spatial- resolution image ILt ;
generating a refined registration model MLHt between the low-spatial-resolution image ILt and the high-spatial-resolution image IHt based on the rough registration model MLH1t and the adjusted image IL—adjt by a pixel-based alignment algorithm, and
the step of generating a rough registration model MLH1t further comprises:
calculating SURF or SIFT key points of the target regions IL—tart in the low-spatial-resolution image ILt and the high-spatial-resolution image IHt respectively;
calculating a distance between each SURF or SIFT key point of the high-spatial-resolution image IHt and each SURF or SIFT key point of the target region IL—tart in the low-spatial-resolution image ILt , to generate matched SURF or SIFT key points between the high-spatial-resolution image IHt and the target region IL—tart in the low-spatial-resolution image ILt ; and
generating the rough registration model MLH1t based on the matched SURF or SIFT key points.

2. The method according to claim 1, wherein the first camera is a static camera or an active camera, and the second camera is an active camera.

3. The method according to claim 1, wherein the step of generating an adjusted image IL—adjt further comprises:
selecting an intensity mapping region, and obtaining cumulative intensity histograms of the high-spatial-resolution image IHt and the low-spatial-resolution image ILt;
generating an intensity mapping model MI(k) between the high-spatial-resolution image IHt and the low-spatial-resolution image ILt; and
adjusting the intensity of the low-spatial-resolution image ILt based on the intensity mapping model MI(k) to generate the adjusted image IL—adjt .

4. The method according to claim 1, wherein the step of generating a refined registration model MLHt further comprises:
converting the high-spatial-resolution image IHt based on the rough registration model MLH1t to generate an image IH—adjt ;
generating a model Mrefinedt based on the image IH—adjt and the adjusted image IL—adjt ;
generating the refined registration model MLHt based on the rough registration model MLH1t and the model Mrefinedt , where MLHt =MLH1t Mrefinedt .

5. The method according to claim 1 or 2, wherein the step 4) further comprises:
inpainting the output image Ioutt based on strategies 1 to 4 to obtain inpainted regions R1 to R4 respectively, where:
the strategy 1 includes: inpainting the output image Ioutt according to the registration model MLH1 and the high-spatial-resolution image IHt in the case of the registration model MLHt being valid;
the strategy 2 includes: inpainting the output image Ioutt using a background image IHBt of the high-spatial-resolution image IHt in the case of the remaining region of the output image Ioutt containing background pixels;
the strategy 3 includes: inpainting the output image Ioutt according to a method based on reference sample patch and relative motion field in the case of the registration model MLHt being invalid, the high-spatial-resolution image IHt not containing the entire moving target or the high-spatial-resolution image IHt being a blurred image;
the strategy 4 includes: inpainting the remaining region of the output image Ioutt which is not inpainted by the strategies 1 to 3 using the low-spatial-resolution image ILt .

6. The method according to claim 5, wherein the step of inpainting the output image Ioutt according to a method based on reference sample patch and relative motion field further comprises the steps of:
establishing and updating a reference frame sequence;
selecting a reference frame SPreft from the reference frame sequence;
generating an inter-frame high-spatial-resolution optical-flow field VH and an inner-frame low-spatial-resolution optical-flow field VL based on the reference frame sequence;
generating an optical-flow field FHt between the reference frame SPreft and the output image Ioutt based on the inter-frame high-spatial-resolution optical-flow field VH and the inner-frame low-spatial-resolution optical-flow field VL; and
performing foreground inpainting of the output image Ioutt based on the optical -flow field FHt and the reference frame sequence.

7. The method according to claim 5, the step 4) further comprising:
post processing the inpainted image to adjust the intensity and spatial continuity of the output image Ioutt after inpainting the output image Ioutt based on the registration model MLHt and the high-spatial-resolution image IHt .

8. The method according to claim 7, wherein the step of adjusting the intensity of the output image Ioutt further comprises:
for the region R1 of the output image Ioutt , calculating the intensity mapping model using pixels of a region overlapped with the background image IHBt only, and adjusting the pixels of the region overlapped with the background image IHBt only; and
for the region R4 of the output Ioutt , calculating the intensity mapping model using pixels of a region overlapped with the background image IHBt , only, and adjusting all the pixels of the output image Ioutt .

9. The method according to claim 7, wherein the step of adjusting the spatial continuity of the output image Ioutt further comprises:
for the regions R1, R2 and R4 of the output image Ioutt, expanding the boundaries of the regions R1, R2 and R4 by a 5×5 structuring element respectively;
smoothing the expanded boundaries of the regions R1, R2 and R4 by a 3×3 mean filter respectively; and
keeping the region R3 of the output image Ioutt unchanged.