JP2010134606A - Device, method, and program for detecting three-dimensional motion - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a device, method, and program for stably detecting a three-dimensional motion of a dynamic object captured by a single camera, with high degree of accuracy. <P>SOLUTION: An integration of a coordinate transformation model into an optical flow method enables a three-dimensional detection of the motion of a dynamic object from a two-dimensional image captured by a single camera. Since the rotational component and the translational component to the motion of the dynamic object are added to restricting conditions in detecting a three-dimensional motion, the motion of the dynamic object is stable detected with a high degree of accuracy. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a technique of a three-dimensional motion detection apparatus, a three-dimensional motion detection method, and a three-dimensional motion detection program for three-dimensionally detecting a motion of a dynamic object photographed by a camera.

  Currently, there are various image processing techniques for detecting the movement of a dynamic object photographed by a camera. As the most basic motion detection method, for example, as represented by MPEG, a cross-correlation method (CC method, pattern matching method) used in the coding field is known. The cross-correlation method is a method in which similarity between two temporally continuous image frames is evaluated using a cross-correlation function or the like, and a similar region is regarded as a moving distance (vector) between image frames. In order to increase the calculation accuracy regarding similarity, there is also a method of dividing an image frame into a plurality of blocks and obtaining a moving distance for each sub-block.

  In particular, in the computer vision field, an optical flow method (see Non-Patent Document 1) is known. The basic equation of optical flow based on the velocity vector of a dynamic object is derived using two or more consecutive image frames, and a constraint equation is generated assuming that the luminance fluctuation between image frames is constant (zero). In this method, the velocity vector is obtained by approximating the constraint condition expression using the least square method (see Non-Patent Document 2), and detected as the motion of the dynamic object.

However, such a conventional image processing technique is a result of projecting the movement of a dynamic object that is originally moving three-dimensionally onto a two-dimensional image plane, and therefore information on the depth direction is taken into consideration. It is not. In order to make this depth information a constituent requirement related to the movement of a dynamic object and to capture the dynamic object in a three-dimensional (three-dimensional) manner, a method using the principle of stereoscopic vision is generally used (Non-Patent Document). 3).
JL Barron, two others, "Performance of Optical Flow Techniques", IJCV, 1994, vol.12, no.1, p.43-77 Toru Nakagawa and 1 other, “Experimental Data Analysis by Least Squares Method”, The University of Tokyo Press, 1995, p.168-169 “3D principle”, [online], [December 1, 2008 search], Internet <URL: http://www2.aimnet.ne.jp/nakahara/3dart/3genri.html>

  However, since the principle of stereoscopic vision requires two or more cameras, it takes a lot of time and effort to secure a place to install multiple cameras and to calibrate multiple videos taken by each camera. There was a problem. Although it is possible to detect the movement of a dynamic object with a single camera using the principle of motion vision, it is necessary to move the camera mechanically, which requires a large and expensive device. There was a problem. Furthermore, there is a problem that depth information cannot be obtained only by using a simple time difference.

  The present invention has been made in view of the above, and a three-dimensional motion detection apparatus and a three-dimensional motion detection apparatus that detect a three-dimensional motion of a dynamic object photographed by one camera with high accuracy and stability. It is an object of the present invention to provide a motion detection method and a three-dimensional motion detection program.

  The invention according to the first claim includes input means for inputting a video of a dynamic object photographed by a single photographing device, storage means for storing the video as a plurality of time-series image frames, and tertiary Conversion formula deriving means for deriving a spatial coordinate conversion formula for converting the position coordinate of the original space into the position coordinate of the two-dimensional space, and partial differentiation of the spatial coordinate conversion formula with respect to time to rotate the motion of an arbitrary object in the three-dimensional space Using a linear model expression expressed by motion and translational motion, a speed vector calculation formula deriving means for deriving the speed vector calculation formula of the arbitrary object on two-dimensional coordinates, and two temporally continuous sheets from the storage means The image frame is read out, the temporal luminance fluctuation component of each pixel forming the dynamic object, the smoothness component of the speed of the dynamic object, and the rotation with respect to the movement of the dynamic object Ingredients and average The dynamic object on two-dimensional coordinates using an optical flow method by substituting the velocity vector calculation formula into the velocity component constituting the luminance variation component and the smoothness component. 3D motion detection means for three-dimensionally detecting the motion of the object and a plurality of the image frames stored in the storage means, the detected three-dimensional motion of the dynamic object is displayed in time series. And a display means.

  According to a second aspect of the present invention, a step of inputting an image of a dynamic object photographed by a single photographing device by a computer and storing the image in a storage means as a plurality of time-series image frames. A step, a step of deriving a spatial coordinate conversion formula for converting a position coordinate in the three-dimensional space into a position coordinate in the two-dimensional space; Deriving the velocity vector calculation formula of the arbitrary object on two-dimensional coordinates using a linear model expression represented by rotational motion and translation motion, and two temporally continuous image frames from the storage means Read out, the temporal luminance fluctuation component of each pixel forming the dynamic object, the smoothness component of the speed of the dynamic object, and the rotation component with respect to the movement of the dynamic object And the translation component as constraint conditions, substituting the velocity vector calculation formula into the velocity component constituting the luminance variation component and the smoothness component, and using the optical flow method to Using the step of three-dimensionally detecting the movement of the object and a plurality of the image frames stored in the storage means, the detected three-dimensional movement of the dynamic object is displayed in time series. And a step.

  The gist of the third aspect of the invention is to cause each step in the three-dimensional motion detection method of the second aspect to be executed by a computer.

  According to the present invention, a three-dimensional motion detection apparatus, a three-dimensional motion detection method, and a three-dimensional motion that detect a three-dimensional motion of a dynamic object photographed by one camera with high accuracy and stability. A detection program can be provided.

  FIG. 1 is a functional configuration diagram illustrating a functional configuration of the three-dimensional motion detection apparatus according to the present embodiment. The three-dimensional motion detection apparatus 100 includes an input unit 11, a conversion formula deriving unit 12, a velocity vector calculation formula deriving unit 13, a motion detecting unit 14, a display unit 15, and a storage unit 31. .

  The input unit 11 has a function of accepting input of an image of a dynamic object photographed by one camera (imaging device) 200.

  The storage unit 31 has a function of storing a video input after being received by the input unit 11 as a plurality of time-series image frames. As such a storage unit 31, for example, a storage device such as a memory or a hard disk is generally used, and not only inside the three-dimensional motion detection device 100 but also via a communication network such as the Internet or a telephone line. It is also possible to use an external storage device that can be electrically connected.

The conversion formula deriving unit 12 has a function of deriving a spatial coordinate conversion formula that converts position coordinates in the three-dimensional space into position coordinates in the two-dimensional space. Specifically, the position coordinates of an arbitrary object in the three-dimensional space are (X1, X2, X3), and the position coordinates when the arbitrary object is projected on the two-dimensional image plane formed by the X1 axis-X2 axis are shown. As (x1, x2), the spatial coordinate conversion formula shown in Formula (1) is derived.

The speed vector calculation formula deriving unit 13 performs partial differentiation on the space coordinate conversion formula derived by the conversion formula deriving unit 12 with respect to time, and obtains a linear model formula representing the motion of an arbitrary object in the three-dimensional space by rotational motion and translational motion. And a function for deriving a velocity vector calculation formula for an arbitrary object on two-dimensional coordinates. Specifically, first, in order to integrate into the optical flow method in the motion detection unit 14 to be described later, the partial differentiation with respect to time is performed on the equation (1) to derive the equation (2). Note that “·” given above each variable indicates time differentiation.

Next, assuming that the motion of an arbitrary object is composed of a rotational motion Ω = (Ω ₁ , Ω ₂ , Ω ₃ ) and a translational motion T = (T ₁ , T ₂ , T ₃ ), Equation (3) shows Such a linear model formula can be used.

Then, using the equations (2) and (3), the equations (4) and (5) can be derived.

The calculation results obtained by the equations (4) and (5) are the velocity vector d (u, v) of an arbitrary object when the position coordinates in the three-dimensional space are converted into the position coordinates of the two-dimensional image as the two-dimensional space. Therefore, the relationship shown in Expression (6) and Expression (7) is established.

  The motion detection unit 14 reads two temporally continuous image frames from the storage unit 31, and temporal luminance fluctuation components of each pixel forming the dynamic object, and the dynamic object Using the smoothness component of velocity and the rotation component and translation component for the movement of the dynamic object as constraints, substituting the velocity vector calculation formula into the velocity component constituting the luminance fluctuation component and smoothness component, It has a function to detect the movement of a dynamic object on a two-dimensional coordinate three-dimensionally using the optical flow method. Hereinafter, the process in the motion detection part 14 is demonstrated concretely.

The optical flow means an apparent velocity vector of a captured dynamic object (see Non-Patent Document 1). As shown in FIG. 2, when the luminance (shading) I at the coordinate (x, y) at the time t moves to the coordinate (x + δx, y + δy) after δt time has passed, the latter (x, y, t) Equation (8) can be obtained by Taylor expansion around.

Since the value obtained by dividing the second term to the fourth term on the right side in equation (8) by δt corresponds to the luminance variation amount between the two image frames, this luminance variation amount is set to 0 (zero). (9) is derived.

Here, when δt → 0 is set as the limit of δt, Equation (10) can be obtained.

Expression (10) is a basic expression for calculating the optical flow, and dx / dt and dy / dt are velocity vectors d (u, v) as shown in Expression (11).

  Here, it is necessary to give a constraint condition in order to calculate this basic formula. However, in this embodiment, the brightness fluctuation condition (brightness fluctuation component) and the smoothness condition of the speed of the dynamic object (dynamic object) The smoothness component of the speed of the object) and the rotation component and the translation component with respect to the movement of the dynamic object are the constraint conditions.

First, the luminance variation condition will be described. It is shown in equation (10) to _{(∂x / ∂t) I x,} (∂y / ∂t) and _I y, the (∂I / ∂t) and _{I t,} using equation (11), the error err Is defined as in equation (12).

When each variable of Expression (11) is discretized on the (i, j) lattice based on the difference method, the error err _{i, j} in the pixel (i, j) can be expressed by Expression (13). This equation (13) is set as a luminance variation condition.

Next, the smoothness condition for the movement of the dynamic object will be described. With respect to this smoothness component, assuming that the movement of the dynamic object is minute and that the spatial variation of the velocity component is smooth, the velocity vector d (u, v, as shown in equation (14). ) Is used. This equation (14) is defined as a smoothness condition.

In the present embodiment, since a three-dimensional motion component is detected from only limited image information of two consecutive image frames, other than the constraint conditions relating to the speed shown in Expression (13) and Expression (14) It is necessary to add conditions. Therefore, as shown in the equations (15) and (16), the rotation component (ΔΩ) and the translation component (ΔT) in the dynamic object are added as constraint conditions. Note that M is the number of pixels adjacent to the pixels forming the dynamic object (for example, 4 to 8 points), and C is the number of pixels located in the vicinity of M.

Finally, the luminance fluctuation condition shown in Expression (13), the smooth condition of the movement of the dynamic object shown in Expression (14), the rotational component of the dynamic object shown in Expression (15), and Expression (16) ) Using the constraint condition (Equation (17)) obtained from the translational component of the dynamic object shown in), the three unknowns (u, v) are converted into the least square method (see Non-Patent Document 2) or the gradient method. Use to estimate. Note that λ ₁ , λ ₂ , and λ ₃ (for example, λ ₁ = 10.0, λ ₂ = λ ₃ = 5.0 can be used) are adjustment coefficients for the smoothness condition and the like. Φ indicates each image area (for example, 10 × 10 pixels) when the image frame is divided into small blocks.

As for the solution of the least square method and the gradient method, eight linear simultaneous systems obtained by partially differentiating Equation (17) with unknown numbers (u, v, Ω ₁ , Ω ₂ , Ω ₃ , T ₁ , T ₂ , T ₃ ). It can be obtained by calculating an equation. In the present embodiment, in order to detect the three-dimensional movement of the dynamic object, u obtained from the equations (4) and (6), v obtained from the equations (5) and (7), and Is substituted into the velocity components u and v constituting the luminance variation condition and smoothness condition in the constraint condition expression shown in Expression (17) to calculate the simultaneous equations. In addition, in order for the above eight simultaneous equations to have the minimum value, it is only necessary that the primary differential value of the eight unknowns be 0 (zero), so it is added that the equation (18) is a condition.

For reference, a specific solution of equation (17) using the gradient method will be described. In the case of the gradient method, assuming that one of the eight unknowns is A, Equation (19) is assumed to be satisfied, and iterative calculation is performed until Equation (19) converges.

Note that p is the number of iterations, B is the first derivative of A (eight unknowns) shown in Expression (18), and μ is an adjustment coefficient. The initial value A (p) = 0 is set, and 8 unknowns are respectively applied to the equation (19) to perform iterative calculation. For example, when the relationship (repetition error) shown in the equation (20) is established, the convergence is achieved. The value obtained at that time is regarded as each unknown value.

  The display unit 15 has a function of displaying the three-dimensional motion of the dynamic object detected by the motion detection unit 14 in time series using arrows or the like using a plurality of image frames stored in the storage unit 31. ing.

  Subsequently, a processing flow of the three-dimensional motion detection apparatus according to the present embodiment will be described. First, the input unit 11 receives an input of an image of a dynamic object photographed by one camera (step S101).

  Next, the storage unit 31 stores the received and input video as a plurality of time-series image frames (step S102).

  Subsequently, the conversion formula deriving unit 12 derives a spatial coordinate conversion formula for converting the position coordinates in the three-dimensional space into the position coordinates in the two-dimensional space (step S103).

  In addition, the velocity vector calculation formula deriving unit 13 performs partial differentiation on the space coordinate conversion formula derived in step S103 with respect to time, and uses a linear model formula that represents the motion of an arbitrary object in the three-dimensional space by rotational motion and translational motion. Thus, a velocity vector calculation formula for an arbitrary object on two-dimensional coordinates is derived (step S104).

  Thereafter, the motion detection unit 14 reads two temporally continuous image frames stored in the storage unit 31, and temporal luminance variation components of the pixels forming the dynamic object; The velocity smoothness component of the dynamic object and the rotation component and translation component with respect to the movement of the dynamic object are used as constraint conditions, and the velocity vector calculation formula derived in step S104 is used as the luminance fluctuation component and the smoothness component. The motion of the dynamic object on the two-dimensional coordinates is detected three-dimensionally using the optical flow method (step S105). The motion detection unit 14 detects motion from two image frames (for example, image frames at time t-1 and time t), and then detects two consecutive image frames (for example, time t and The three-dimensional motion of the dynamic object is repeatedly detected using time t + 1).

  Finally, the display unit 15 displays the three-dimensional motion of the dynamic object detected by the motion detection unit 14 in time series (step S106).

  Note that step S103 and step S104 may be processed at any stage as long as they are before step S102.

  FIG. 3 is a diagram showing the detection results of the movement of the dynamic object whose center is moving in the depth direction by arrows. In the case of the present embodiment shown in FIG. 3 (a), the movement of the moving object moving in the center depth direction with the passage of time is indicated by an arrow from the periphery toward the center direction. It can be understood that the movement of the moving object heading toward is properly detected. On the other hand, in the case of the conventional method shown in FIG. 3B (two-dimensional estimation method of Horn & Schunkck method), only the arrows from the left and right to the center direction are detected, and the state from the vertical direction to the center direction is shown. Since there is no depth component, it can be seen that almost no depth component is detected. From this detection result, the effectiveness of the three-dimensional modeling in the present invention can be evaluated.

  FIG. 4 is a diagram showing detection results of hurricane movements with prominent three-dimensional structures by arrows. In the case of the present embodiment shown in the upper right of FIG. 4, it can be understood that the state of turning is clearly detected as a feature of the vortex. Moreover, the change of the depth direction accompanying the convection structure around the vortex is well captured. On the other hand, in the case of the conventional method shown in the lower right of FIG. 4 (the two-dimensional estimation method of Lucas-Kanade method), only the movement of the outer frame of the vortex is detected, and the characteristics of the entire vortex are sufficiently obtained. It turns out that it has not detected. From this detection result, it can be said that even a two-dimensional hurricane image with a change in the depth direction can also detect a three-dimensional feature.

  According to the present embodiment, since the coordinate transformation model is fused with the optical flow method, the movement of the dynamic object is detected three-dimensionally even from a two-dimensional image photographed with one camera. It becomes possible to do. In addition, when detecting three-dimensional movement, the rotational and translational components for the movement of the dynamic object are added to the constraint conditions, so the movement of the dynamic object can be detected accurately and stably. Is possible.

  Finally, the three-dimensional motion detection apparatus described in each embodiment is configured by a computer, and each process of each functional block is executed by a program. Further, each processing operation of the three-dimensional motion detection apparatus described in each embodiment is recorded as a program on a recording medium such as a compact disk or a floppy (registered trademark) disk, and this recording medium is incorporated in a computer. Alternatively, a program recorded on a recording medium can be downloaded to a computer via an arbitrary communication line, or installed from the recording medium, and the computer can be operated by the program to perform the above-described processing operations in a three-dimensional manner. Of course, it can function as a detection device.

  Note that the three-dimensional motion detection apparatus described in this embodiment can be applied to the image processing field, the weather field, the river field, a field mainly for monitoring work in these fields, and a communication field related to video distribution. Let me add.

It is a functional block diagram which shows the function structure of the three-dimensional motion detection apparatus which concerns on this Embodiment. It is a figure which shows the movement of the brightness of micro time. It is the figure which showed the detection result of the motion of the dynamic target object which the center part is moving in the depth direction by the arrow. It is the figure which showed the detection result of the motion of a hurricane with a prominent three-dimensional structure by the arrow.

Explanation of symbols

DESCRIPTION OF SYMBOLS 11 ... Input part 12 ... Conversion formula derivation part 13 ... Speed vector calculation formula derivation part 14 ... Motion detection part 15 ... Display part 31 ... Storage part 100 ... Three-dimensional motion detection apparatus 200 ... Camera S101-S106 ... Step

Claims (3)

An input means for inputting an image of a dynamic object photographed by one photographing apparatus;
Storage means for storing the video as a plurality of time-series image frames;
A conversion formula deriving means for deriving a spatial coordinate conversion formula for converting the position coordinates of the three-dimensional space into the position coordinates of the two-dimensional space;
Using the linear model formula that partially differentiates the spatial coordinate conversion formula with time and expresses the motion of the arbitrary object in the three-dimensional space by the rotational motion and the translational motion, the velocity vector calculation formula of the arbitrary target on the two-dimensional coordinates is A speed vector calculation formula deriving means for deriving;
The two temporally continuous image frames are read out from the storage means, and the temporal luminance fluctuation component of each pixel forming the dynamic object and the smoothness of the speed of the dynamic object And a rotation component and a translation component with respect to the motion of the dynamic object, and the velocity vector calculation formula is substituted into the velocity component constituting the luminance variation component and the smoothness component to obtain an optical A motion detection means for three-dimensionally detecting the motion of the dynamic object on two-dimensional coordinates using a flow method;
Display means for displaying a three-dimensional movement of the detected dynamic object in time series using the plurality of image frames stored in the storage means;
A three-dimensional motion detection apparatus comprising: By computer
Inputting an image of a dynamic object photographed by a single photographing device;
Storing the video in a storage means as a plurality of time-series image frames;
Deriving a spatial coordinate conversion formula for converting the position coordinates of the three-dimensional space into the position coordinates of the two-dimensional space;
Using the linear model formula that partially differentiates the spatial coordinate conversion formula with time and expresses the motion of the arbitrary object in the three-dimensional space by the rotational motion and the translational motion, the velocity vector calculation formula of the arbitrary target on the two-dimensional coordinates is Deriving steps;
The two temporally continuous image frames are read out from the storage means, and the temporal luminance fluctuation component of each pixel forming the dynamic object and the smoothness of the speed of the dynamic object And a rotation component and a translation component with respect to the motion of the dynamic object, and the velocity vector calculation formula is substituted into the velocity component constituting the luminance variation component and the smoothness component to obtain an optical Detecting three-dimensionally the movement of the dynamic object on two-dimensional coordinates using a flow method;
Displaying the detected three-dimensional movement of the dynamic object in time series using the plurality of image frames stored in the storage means;
A three-dimensional motion detection method comprising:   A three-dimensional motion detection program for causing a computer to execute each step in the three-dimensional motion detection method according to claim 2.