JP2013156855A - Method for tracking mobile object and computer program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To reduce a calculation cost required for tracking process of a moving mobile body.SOLUTION: A method for tracking a mobile object includes: acquiring a frame image in chronological order from an image photographed with a camera; detecting foreground pixels that are pixels corresponding to a mobile object, from the frame image; projecting the detected foreground pixels on one dimension on the basis of a scale conversion coefficient prestored by a scale conversion coefficient storage unit; detecting a peak position of a projection value obtained by a one-dimensional projection step; and tracking a peak position by associating a peak position detected in a previous frame image with the peak position detected by the current frame image.

Description

  The present invention relates to a technique for tracking a moving object moving in a place by analyzing an image photographed by a camera.

  In order to measure the number of passing people using a camera image, it is necessary to detect that a person has appeared on the image and that a person has passed. Various image processing techniques for realizing such detection have been proposed. These techniques can be roughly divided into two because of the difference in the preconditions.

  The first technique is a case where the camera is directed almost directly downward from the ceiling or the like (see, for example, Non-Patent Document 1). In this case, since it is possible to shoot in a form in which the persons do not overlap each other on the image, it can be detected relatively easily that the person has appeared on the image and passed.

  However, in the above-mentioned Non-Patent Document 1, since it is assumed that the image is taken almost directly downward, there is a problem that the range in which an image can be taken without people overlapping becomes narrow, or a problem that it cannot be applied when the camera cannot be fixed to the ceiling or the like. There is.

  On the other hand, a technique based on the premise that an image is taken obliquely downward (for example, around 30 degrees obliquely downward) like a general security camera has been proposed (see, for example, Non-Patent Document 2). Thus, if measurement with a diagonally downward camera becomes possible, the application scene will spread greatly.

Satoshi Sato and three others, “Animal counting method that is robust to fluctuations in body flow”, Proceedings of the IEICE Autumn Conference, 1994, Information and Systems, 271 Tatsuya Osawa, 5 others, “Person Tracking Using 3D Model for Object and Environment Based on MCMC Method”, IEICE Transactions, 2008, VOL. J91-D, no. 8, pp. 2137-2147

  The technique of Non-Patent Document 2 is a technique for tracking (tracking) a two-dimensional position of a subject on an image or in real space in time series. However, there is a problem in that the calculation cost is inevitably increased if two-dimensional search and association are performed while allowing overlapping of persons on the image.

In recent years, it has become possible to perform tracking processing in real time as computers become faster. However, when it is desired to process images from a plurality of cameras at the same time, the calculation cost is still a big problem. Under such circumstances, there is an expectation for realizing a tracking process with a lower calculation cost.
In view of the above circumstances, an object of the present invention is to provide a technique for reducing the calculation cost required for tracking processing of a moving mobile object.

  One aspect of the present invention includes a frame image acquisition step of acquiring frame images in time-series order from video captured by a camera, a foreground detection step of detecting foreground pixels that are pixels corresponding to a moving object from the frame image, and a scale. A one-dimensional projection step for projecting the foreground pixels detected in the foreground detection step one-dimensionally based on a scale conversion factor stored in advance by a transformation coefficient storage unit, and a projection value obtained by the one-dimensional projection step A peak position that tracks the peak position by associating the peak position detected in the previous frame image with the peak position detected in the current frame image. And a tracking step.

  One aspect of the present invention is the mobile tracking method described above, wherein an optical flow calculation step of calculating an optical flow in the frame image and in which direction on the one-dimensional the peak position is moving A moving direction detection step for estimating based on an optical flow, and in the peak position tracking step, the peak position detected in the previous frame image, and the peak position detected in the current frame image; Are matched based on the estimated direction.

  One embodiment of the present invention is a computer program for causing a computer to execute the above moving body tracking method.

  According to the present invention, it is possible to reduce the calculation cost required for the tracking process of a moving mobile object.

It is a conceptual diagram which shows the example of arrangement | positioning of the passage number measuring device by 1st Embodiment of this invention. It is a schematic block diagram which shows the function structure of the passage number measurement apparatus 1 by 1st Embodiment of this invention. It is a flowchart for demonstrating the flow of a process of the passing person counting method by 1st Embodiment of this invention. It is a figure which shows the specific example of a foreground pixel (silhouette). It is a conceptual diagram for demonstrating the calculation method of the one-dimensional projection by 1st Embodiment of this invention. It is a conceptual diagram which shows an example of the process of the one-dimensional projection by 1st Embodiment of this invention. It is a conceptual diagram for demonstrating the detection method (generation of a tracking candidate peak) of the peak position by 1st Embodiment of this invention. It is a conceptual diagram for demonstrating the tracking process in 1st Embodiment of this invention. It is a conceptual diagram for demonstrating the number estimation method of the tracking object by 1st Embodiment of this invention. It is a schematic block diagram which shows the function structure of the passage number measuring device 1 by 2nd Embodiment of this invention. It is a flowchart for demonstrating the flow of a process of the passing person counting method by 2nd Embodiment of this invention. It is a conceptual diagram which shows an example of the process of the one-dimensional projection by 2nd Embodiment of this invention.

[First Embodiment]
First, a first embodiment of the present invention will be described.
FIG. 1 is a conceptual diagram showing an arrangement example of the passing number measuring device according to the first embodiment of the present invention. The passing person counting apparatus described below is an apparatus that measures the number of moving bodies that have passed through an area photographed by the camera 2 by tracking the moving bodies by applying the moving body tracking method of the present invention. In the following description, a person is used as a specific example of the moving body, but the moving body may be an object other than a person such as a car or an animal.

  The passing number measuring device 1 includes a camera 2 and an information processing device 3. In the example of FIG. 1, video data (image data) captured by the camera 2 is input to an information processing apparatus (personal computer or the like) 3, and the number of passing people is measured by performing image processing on the information processing apparatus 3. Shows the case. As shown in FIG. 1, in the first embodiment of the present invention, the camera 2 captures a target area obliquely downward like a general security camera. Then, the information processing device 3 measures the persons P1 to P3 moving within the target area.

  FIG. 2 is a schematic block diagram showing a functional configuration of the passing number measuring device 1 according to the first embodiment of the present invention. The information processing device 3 includes a CPU (Central Processing Unit), a memory, an auxiliary storage device, and the like connected by a bus, and executes a passing-person counting program. The information processing device 3 executes the frame number acquisition unit 30, the video storage unit 31, the foreground detection unit 32, the one-dimensional projection unit 33, the scale conversion coefficient storage unit 34, the peak position detection unit 35, the peak by executing the passing number measurement program. It functions as an apparatus including a position tracking unit 36, a peak number of people estimation unit 37, and a tracking result storage unit 38. All or some of the functions of the information processing apparatus 3 may be realized by using hardware such as an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA). . The passing person counting program may be recorded on a computer-readable recording medium. The computer-readable recording medium is, for example, a portable medium such as a flexible disk, a magneto-optical disk, a ROM, a CD-ROM, or a storage device such as a hard disk built in the computer system.

  The first embodiment of the present invention may be configured as a real-time type that performs processing while acquiring video data from the camera 2 by the information processing device 3 one by one, or recorded in the video storage unit 31 of the information processing device 3 You may comprise as a batch type processed while reading video data. In the case of a real-time configuration, the video storage unit 31 is not an essential configuration. In the case of a batch type configuration, the camera 2 does not need to be connected to the frame image acquisition unit 30. In the case of the batch type, the video data recorded in the video storage unit 31 is video data taken by the camera 2.

  The frame image acquisition unit 30 inputs frame images of video data captured by the camera 2 in time series. The foreground detection unit 32 detects foreground pixels that are pixels corresponding to a moving object such as a person from the input frame image. The one-dimensional projection unit 33 refers to the scale conversion coefficient stored in advance in the scale conversion coefficient storage unit 34 and projects the foreground pixels onto the one-dimensional line.

  The peak position detector 35 detects the peak position from the projection value. The peak position is a position on a one-dimensional line that gives a peak (maximum value). The peak position tracking unit 36 tracks the peak position detected in the previous frame image in association with the peak position detected in the current frame. The number-of-peaks estimation unit 37 estimates the number of people belonging to the peak from the magnitude of the projection value for each peak of the projection value. The tracking result storage unit 38 stores the appearance and disappearance of peaks and the estimated number of people for each peak as tracking result data.

  The video storage unit 31, the scale conversion coefficient storage unit 34, and the tracking result storage unit 38 are storage devices 40 such as a magnetic hard disk device and a semiconductor storage device. The storage device 40 may be included in the information processing device 3, may be connected to the outside of the information processing device 3, or may be a removable external memory (such as a card-type recording medium or a USB memory). Good.

Next, the operation of the first embodiment of the present invention will be described.
FIG. 3 is a flowchart for explaining the flow of processing of the passing person counting method according to the first embodiment of the present invention. The passing person counting method according to the first embodiment of the present invention includes a process at the time of initial setting and a process at the time of actual measurement which may be performed once before performing the measurement.

  First, an outline of processing at the time of initial setting will be described. In the process at the time of initial setting, the camera 2 captures a subject having a known geometric property and acquires a frame image (step Sa1). Next, the information processing device 3 analyzes the photographed subject image and calculates camera parameters (step Sa2). That is, the information processing device 3 calculates the relationship between the real space photographed by the camera 2 and the camera 2 (where each pixel is photographed in the real space) by analyzing the photographed subject image. . The process of step Sa2 is so-called camera calibration.

  In camera calibration, external parameter estimation and internal parameter estimation are performed. In the estimation of the external parameters, the three-dimensional position (X, Y, Z) of the camera 2 in the real space and the direction (θ, φ, ω) of the camera 2 are obtained. In the three-dimensional position (X, Y, Z), for example, X and Y are two-dimensional positions on the floor surface, and Z represents the height from the floor surface. In the direction (θ, φ, ω), for example, θ represents rotation about the X axis, φ represents rotation about the Y axis, and ω represents rotation about the Z axis. In the estimation of the internal parameters, the focal length and distortion of the optical system of the camera 2 are obtained. The information processing apparatus 3 can determine where each pixel of the frame image is captured in the real space by performing both of these estimations.

  The information required in the first embodiment of the present invention is what angle each pixel of the camera 2 is with respect to the floor surface. Of the three-dimensional position (X, Y, Z), the value of the two-dimensional coordinate (X, Y) indicating the floor surface coordinate becomes unnecessary. Various techniques exist for camera calibration for calculating external parameters and internal parameters of the camera 2. Any technique may be applied in the present embodiment.

  References describing camera calibration include the following references. Reference 1 (Isao Miyagawa, two others, “Simple Camera Calibration From a Single Image Using Five Points on Two Orthogonal 1-D Objects”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 6, JUNE 2010) Reference 2 (JP 2010-287074 A), Reference 3 (JP 2011-215082 A).

  The information processing device 3 calculates a scale conversion coefficient for normalizing the difference in size with respect to the position on the image based on the camera calibration result (step Sa3). On the image photographed by the camera 2, the person located in front will occupy a large area on the image, and the person located far away will occupy a small area on the image. Therefore, the information processing device 3 calculates the scale conversion coefficient by quantitatively calculating how the size of the person shown in the frame image changes in the place on the image.

  More specifically, the information processing apparatus 3 obtains a scale conversion coefficient w (x, y) for each pixel (x, y) using the camera parameters (internal parameters and external parameters). The scale conversion coefficient w (x, y) is calculated based on the geometric relationship between each pixel and the surface area in the real space taken by the pixel. For example, when a silhouette for one person is shown somewhere in the image, the expression (1) is established for a set f of pixels constituting the silhouette.

  The specific calculation method of the scale conversion coefficient is described in Reference Document 4 (Hiroyuki Arai and three others, “Study on Geometric Invariant for Counting People from Video”, Eiji Jikho, Vol. 34, no. 45, ME 2010-163, pp. 41-45) and the literature described in Reference 4. Any technique may be applied in the present invention.

  FIG. 4 is a diagram illustrating a specific example of foreground pixels (silhouettes). The set of foreground pixels for one person in FIG. 4A is represented as set f, the set of foreground pixels for one person in FIG. 4B is represented as set f ′, and the foreground pixel for one person in FIG. It is shown in Reference Document 4 that s (x, y) (a hat is expressed on s) can be defined such that the following expression 2 holds, if the set is represented as a set f ″. Regardless of the standing position of the subject, there is a numerical value (s (x, y) (hat is shown on s)) that makes the sum constant when taking the sum of the subject portion.

  Here, when the scale conversion coefficient w (x, y) is expressed as in equation (3) using the above s (x, y) (hat is shown on s), equation (4) is established. To do.

In addition, without strictly complying with Reference 4, it is necessary to quantitatively estimate how much surface area each pixel corresponds to in the vicinity of the height of a person in the real space (the extent of the pixel in the real space). Thus, the scale conversion coefficient w (x, y) can also be obtained approximately. The calculated value of the scale conversion coefficient w (x, y) is stored in the scale conversion coefficient storage unit 34 as a scale conversion coefficient table.
The above is the process at the time of initial setting. The process at the time of initial setting is not necessarily executed by the information processing apparatus 3. For example, some or all of the processing may be executed by a human or may be executed by another information processing apparatus.

  Next, processing during actual measurement will be described. The information processing device 3 acquires the video data from the camera 2 or the video storage unit 31, and measures the number of people passing by repeating the following processing (steps Sa10 to Sa18).

  First, the frame image acquisition unit 30 acquires a frame image from the camera 2 or the video storage unit 31 (step Sa10). Next, the foreground detection unit 32 detects the foreground pixel in the current frame image by processing the input frame image (step Sa11). A pixel corresponding to an object that is regularly present at that location is called a background pixel, whereas a pixel corresponding to an object (here mainly a person) that temporarily appears at that location is called a foreground pixel. . Also, an image in which foreground pixels and background pixels are represented by different values is referred to as a “foreground image” in the following description.

  Detection of foreground pixels is a technique that has existed for a long time in the image processing field, and various methods have already been proposed. For example, the foreground detection unit 32 considers a pixel obtained by taking an average value in the time direction (or moving average value in the time direction) of each pixel for a certain past time (for example, about 5 minutes) as a background pixel. The foreground detection unit 32 detects a pixel whose absolute value of the difference between the current frame image and the background pixel is greater than a certain value as a foreground pixel. In the present embodiment, the foreground detection method is not limited.

  Next, the one-dimensional projection unit 33 refers to the scale conversion coefficient stored in the scale conversion coefficient storage unit 34, and projects the detected foreground pixels onto a certain line (for example, the x axis) on the image (step). Sa12). When the detected current foreground image is expressed as in Expression (5), the one-dimensional projection p (x) on the x-axis is expressed as Expression (6). Note that the addition (sigma) in Expression (6) is the sum for all the pixels (x, y) in the image.

  FIGS. 5A to 5C are conceptual diagrams for explaining a one-dimensional projection calculation method according to the first embodiment of the present invention. 5A shows the scale conversion coefficient, FIG. 5B shows the foreground image, and FIG. 5C shows the projection value. First, the one-dimensional projection unit 33 initializes the projection value p (x, y) with 0 (1). Next, when the pixel (x, y) is the foreground (2), the one-dimensional projection unit 33 refers to the scale conversion coefficient w (x, y) (3) and sets the projection value p (x) to w (x , Y) are added (4). The one-dimensional projection unit 33 obtains a one-dimensional projection p (x) on the x-axis by executing the above process on all the pixels (x, y).

  In the projection processing described above, the axis to be projected does not necessarily have to be on the x axis. For example, if the processing is performed after the image has been rotated by a predetermined angle in advance, one-dimensional projection onto an arbitrary straight line can be performed by the same processing as the projection onto the x-axis.

  Which direction is used as the projection axis may be determined by looking at the moving direction of the person in the image. When the passage is photographed from substantially the horizontal direction, the movement of the person on the image is dominant in the left-right direction. For this reason, in consideration of the characteristics of the method described later, projection onto the x-axis (the horizontal direction of the image) is sufficient. On the other hand, when the camera 2 is directed from the front of the passage toward the back, the vertical axis corresponding to the person's traveling direction may be used as the projection axis.

  FIGS. 6A to 6C are conceptual diagrams illustrating an example of a one-dimensional projection process according to the first embodiment of the present invention. 6A is an example of the current frame image, FIG. 6B is an example of the detected current foreground image, and FIG. 6C is an example of a one-dimensional projection when the x axis is the projection axis. is there. FIG. 6C shows the result of smoothing processing (such as a Gaussian filter) in the x-axis direction after projection. It is not always necessary to perform smoothing in the first embodiment of the present invention. However, in order to perform processing described below (in particular, peak position detection) in a practical and stable manner, it is necessary that the projection value P (x) changes smoothly in the x direction. Therefore, smoothing contributes to the improvement of the processing system.

  The smoothing method is not limited in the first embodiment of the present invention. However, it is preferable to apply a method in which the sum of projection values (the sum of P (x) for all x) does not change before and after smoothing. Specifically, a standardized linear filter (a linear filter in which the sum of filter coefficients is normalized to 1) may be used. Hereinafter, the one-dimensional projection value and the value obtained by smoothing the one-dimensional projection value in the x direction are not distinguished from each other, and both are represented as P (x).

  Next, the peak position detector 35 detects the peak position from the one-dimensional projection value P (x) (step Sa13). In the detection of the peak position, if the one-dimensional projection value that becomes the peak does not exceed a preset threshold value, it may be regarded as noise and not detected. In addition, peaks at both ends of the x-axis of the image (when the processing target range is limited in advance, the x-coordinates at both ends) are not detected. The detected peak position can be regarded as a place where a person is likely to be near the x coordinate. Therefore, the peak position detection unit 35 sets the detected peak position as a tracking candidate peak. When a plurality of peak positions are detected, a plurality of tracking candidate peaks are generated.

  FIGS. 7A and 7B are conceptual diagrams for explaining a peak position detection method (generation of tracking candidate peaks) according to the first embodiment of the present invention. FIG. 7A shows a person moving from right to left in the image. FIG. 7B shows a change in the projection value P (x) on the x-axis (the horizontal direction of the image). As shown at the left end of FIG. 7 (A), when there is no person in the image, the peak of the projection value is not detected as shown at the left end of FIG. 7 (B). Further, as shown in the center of FIG. 7A, when a part of the person P exists at the end of the image, the peak of the projection value is not detected as shown in the center of FIG. 7B. As shown at the right end of FIG. 7A, when a person P exists in the image, a peak equal to or higher than the threshold is detected as shown at the right end of FIG.

  Next, the peak position tracking unit 36 performs processing (tracking processing) in which tracking candidate peaks detected in each frame image are associated in the time direction based on the position (step Sa14). Next, the peak number-of-people estimation unit 37 detects how many persons the tracking object obtained by the tracking process is composed of (step Sa15). Then, the peak number of people estimation unit 37 stores (adds) the tracking result in the tracking result storage unit 38 (step Sa16).

  Next, the information processing apparatus 3 determines whether or not an end condition is satisfied, such as an instruction for an end operation by the user (step Sa17). When the end condition is not satisfied (step Sa17-NO), Is processed (step Sa18), the process returns to step Sa10 and the above-described processing is repeated. On the other hand, when the end condition is satisfied (YES in step Sb18), the information processing apparatus 3 ends the process.

Next, the tracking process in step Sa14 described above will be described in detail.
FIG. 8 is a conceptual diagram for explaining the tracking process in the first embodiment of the present invention. 8 is a diagram illustrating a specific example of a frame image (input image) acquired by the information processing device 3. In the following description, as indicated by an arrow, the information processing apparatus 3 acquires a frame image in which a state in which the person P appears from the right end of the image and moves toward the left is captured. FIG. 8 shows an input image at time t.
In FIG. 8, the diagram located below the input image is a diagram showing the projection value at time t. And the figure shown in the lowest stage of FIG. 8 is a figure which shows the spatiotemporal plot which plotted the peak position detected by each frame image in time series order. The horizontal axis represents the x-axis, and the vertical axis represents the time (upward is present and past as it goes down). As the person P moves little by little to the left, the peak position gradually moves to the left. In such a spatiotemporal plot, there are various methods for associating peaks based on peak positions. Hereinafter, an example thereof will be specifically described.

  First, a description will be given in order from the initial state where there is no person. In the initial state where there is no person, no tracking candidate peak exists. Therefore, even if peak detection is performed, a peak exceeding the threshold is not detected. In this case, no further processing is performed. Next, it is assumed that one person appears in the image and a peak is detected. At this time, since there is no person who has been tracked in the past, this peak is defined as one person (= tracking object). As the tracking object data, the tracking object ID (such as a serial number) and its peak position (x coordinate value: x0) are recorded.

  Assume that in the next frame, this person has moved a little and a peak has been detected at another x-coordinate value x1. As the next process, it is determined whether or not the tracking object detected last time (the peak detected in the previous tracking object (x coordinate is x0)) and the currently detected peak x1 correspond to each other. This determination process is performed as follows. First, the distance (difference) on the x axis between x0 and x1 is calculated. Next, it is determined whether this distance is smaller than a predetermined threshold value. When the distance is smaller than the threshold value, it is determined that the peak (x coordinate value x1) detected this time corresponds to the current tracking object.

  In this case, the previously set tracking object is inherited (tracked) to x1. On the other hand, when the distance is larger than the threshold, x1 is determined as a new tracking object. In addition, when a plurality of tracking candidate peaks are at a distance within the threshold, they are inherited (tracked) to the one with the smallest distance.

  In this way, the peak position tracking unit 36 tracks the previous peak position of the tracking object while comparing it with the current tracking candidate position, and generates a new tracking object as necessary. By repeating the above process, when tracking is successful a plurality of times, data of a plurality of peak positions are recorded in the tracking object data. As described above, the peak coordinates of the past several times (the number of times is set in advance) are recorded while the peaks are successively inherited (tracked).

  When tracking is performed a plurality of times and a large number of peak positions (a predetermined number of times or more) are recorded, not a simple position comparison between the previous time and the current time as described above, The current peak position may be predicted. Then, by evaluating whether the distance from the predicted position is within the threshold, more accurate tracking can be performed.

  By referring to the sequence of peak positions in the past several times (for example, three times), the inclination (Δx: how much x changes on average for each frame) in the spatiotemporal image is obtained. Xn + Δx obtained by adding Δx to the previous peak position xn is set as the current predicted position. Then, by setting a value obtained by adding or subtracting a predetermined threshold value to or from the predicted position as both ends of the predicted range, it is possible to perform tracking in the form of taking into account the past several movements.

  In addition, prepare multiple patterns for how many times the peak position is predicted in the past, and if both conditions are met, the peak is inherited (tracked), or if either condition is met Peaks may be inherited (tracked). Thereby, it becomes possible to make a judgment at a higher level (overall judgment from a short-time prediction and a long-time prediction). FIG. 8 shows short-time prediction predicted from the past three peak positions and long-time prediction predicted from the past eight peak positions.

  While performing these processes, the peak position tracking unit 36 deletes the tracking object data when there is no peak corresponding to the tracking object. By sequentially executing the tracking process, the time when the tracking object is generated is assumed to be "person has appeared", and the time when the tracking object is deleted is assumed to be "the person has left", and the number of passing people is measured. be able to.

  Next, the estimation of the number of people for each peak in step Sa15 described above will be described in detail. The tracking object is tracked while inheriting the peak. The peak may correspond to a single person, or may be a group of people moving together (in groups). The peak number of people estimation unit 37 estimates how many people are included in the tracking object.

  FIGS. 9A and 9B are conceptual diagrams for explaining a method of estimating the number of tracking objects according to the first embodiment of the present invention. As shown in FIG. 9 (A), when there is one peak, a range of a predetermined width 2L (each left and right) is set on both sides of the peak a, and the one-dimensional projection value P ( Add the value of x). That is, the projection value p (x) is cumulatively added in the range of x = xa−L to x = xa + L. The result of cumulative addition is an estimated value of the number of people belonging to the peak. Note that such an estimation process is possible on the assumption that Expression (1) holds.

  Further, as shown in FIG. 9B, when the two peaks (peak a and peak b) are at positions within the width L described above, the estimation process may be performed as follows. First, the tracking object is separated at the midpoint between the peak position a and the peak position b. The coordinates of the midpoint (xab) can be expressed as xab = (xa + xb) / 2. Next, for the peak a, the projection value p (x) is cumulatively added in the range of x = xa−L to x = xab. The result of cumulative addition is an estimated value of the number of people belonging to peak a. For the peak b, the projection value p (x) is cumulatively added in the range of x = xab to x = xb + L. The result of the cumulative addition is an estimated value of the number of people belonging to the peak b. Note that, instead of the midpoint xab of the peak a and the peak b, an x coordinate (xmin) that gives a minimum value between the peak a and the peak b may be obtained and divided by xmin.

  A known silhouette (a standard silhouette of a person (shape and size)) and an actual person have a certain difference in shape and size. Therefore, even if one person is shown, the estimated value of the number of people for each peak takes a value close to 1.0 such as 0.85 or 1.17, but does not necessarily match 1.0. . Similarly, in the case of two people, it is close to 2, but not necessarily equal to 2.0, such as 1.82 people.

  Therefore, as described above, the accumulated addition value of p (x) for each peak is not used as the estimated number of people as it is, but may be an estimated value of the number of people as an integer value by rounding off to the first decimal place, for example. By performing such rounding processing for all the peaks of the tracking object, the estimated number of persons in each frame image is calculated. Then, a statistical value (average value, mode value, etc.) of the estimated number of persons from when each tracking object is generated until it disappears may be used as the final number of persons constituting the tracking object. By continuously performing these processes, it is possible to measure how many people have passed through the place within a predetermined time.

  According to the first embodiment described above, by tracking the peak value of the one-dimensional projection value calculated using the scale conversion coefficient obtained from the camera parameter, the calculation cost is reduced in the tracking process of the moving object. It becomes possible.

[Second Embodiment]
Next, a second embodiment of the present invention will be described. Since the second embodiment of the present invention has the same parts as the first embodiment, a supplementary description will be given.

FIG. 10 is a schematic block diagram showing a functional configuration of the passing person counting device 1 according to the second embodiment of the present invention. The same components as those shown in FIG. 2 are denoted by the same reference numerals as those in FIG.
In FIG. 10, an optical flow calculation unit 41 calculates an optical flow that is an apparent velocity field on an image from a plurality of temporally adjacent frame images. The one-dimensional projection unit 42 refers to the scale conversion coefficient stored in advance in the scale conversion coefficient storage unit 34 and projects the foreground pixels and the optical flow onto a one-dimensional line. The peak position / movement direction detection unit 43 detects the peak position from the projection value related to the foreground pixels. Further, the peak position / movement direction detection unit 43 estimates the movement direction indicating in which direction the peak position is moving on the line from the optical flow projection result.

  The peak position tracking unit 44 associates the peak position detected in the previous frame image with the peak position detected in the current frame image. More specifically, the peak position tracking unit 44, in addition to the evaluation criteria for the distance between peaks (whether included in the predicted position) in the inheritance (tracking) of the peak in the first embodiment, the past movement direction Inheritance (tracking) is comprehensively performed based on the evaluation criteria (whether the instantaneous moving direction estimation results are consistent). In other words, the tracking is performed based not only on the peak position but also on the movement direction determined from the optical flow of the peak.

Next, the operation of the second embodiment will be described.
FIG. 11 is a flowchart for explaining a process flow of the passing-person counting method according to the second embodiment of the present invention. The passing person counting method according to the second embodiment of the present invention is similar to the above-described first embodiment. The initial setting process and the actual measurement process may be performed once before the measurement is performed. including. The process at the initial setting shown in steps Sb1 to Sb4 in FIG. 11 is the same as the process at the initial setting shown in steps Sa1 to Sa4 in FIG. 3 in the first embodiment.

  Hereinafter, the process at the time of measurement in 2nd Embodiment is demonstrated. The information processing apparatus 3 acquires the video data from the camera 2 or the video storage unit 31, and measures the number of people passing by repeating the following processing (steps Sb10 to Sb19).

First, the frame image acquisition unit 30 acquires a frame image from the camera 2 or the video storage unit 31 (step Sb10). Next, the foreground detection unit 32 detects the foreground pixel in the current frame image by processing the input frame image (step Sb11). Next, the optical flow calculation unit 41 calculates an optical flow from a plurality of frame images that are close in time (step Sb12).

  The process of calculating the optical flow is a process of calculating an apparent movement (speed field) on the image using two or three or more images that are temporally continuous. The process of calculating the optical flow is known as one of basic processes in the field of image processing. There are various optical flow calculation methods, and the second embodiment of the present invention does not limit the method. By calculating the optical flow, it is possible to obtain what kind of motion is observed at each location (each pixel) on the image. That is, the velocity fields Vx (x, y) and Vy (x, y) can be obtained. Here, Vx represents a velocity component in the x direction, and Vy represents a velocity component in the y direction. Since these are calculated for each pixel (x, y), the velocity field is expressed as Vx (x, y), Vy (x, y).

  Next, the one-dimensional projection unit 42 refers to the scale conversion coefficient stored in the scale conversion coefficient storage unit 34, and displays the detected foreground pixels and the calculated optical flow on a one-dimensional line, for example, the x-axis. Projecting upward (horizontal direction) (step Sb13). More specifically, the one-dimensional projection unit 42 looks at the component in the projection axis direction (in this example, the x-axis direction) for the velocity field calculated in the current frame image, and determines whether this is a positive value or a negative value. It is determined whether it is a value.

  12A to 12D are conceptual diagrams illustrating an example of the one-dimensional projection process according to the second embodiment of the present invention. FIG. 12A shows an example of the current frame image, and FIG. 12B shows an example of the current foreground image detected. FIG. 12C is a diagram in which the pixels in which the rightward velocity field is observed are visualized with diagonal lines and the pixels in which the leftward velocity field is observed among the foreground pixels shown in FIG. 12B. is there. FIGS. 12D and 12E are examples of projected images showing the results of one-dimensional projection. 12D and 12E show the result of smoothing processing (Gaussian filter or the like) in the x-axis direction after projection, as in the first embodiment. The one-dimensional projecting unit 42 performs one-dimensional projection by dividing a positive component and a negative component in the projection axis direction. For example, the one-dimensional projection unit 42 performs one-dimensional projection for only the pixels facing left while referring to the value of w (x, y). An example of the result is FIG. Further, the one-dimensional projecting unit 42 performs the one-dimensional projection for only the right-facing pixels while referring to the value of w (x, y). An example of the result is FIG.

  Next, as in the first embodiment, the peak position / movement direction detection unit 43 detects the peak position from the one-dimensional projection of the foreground, and calculates the projection value of the rightward pixel and the projection value of the leftward pixel at the peak position. Compare. The peak position / movement direction detector 43 sets the larger value as the movement direction estimation result at the peak position (step Sb14). That is, the peak position / movement direction detection unit 43 determines the movement direction (right or left) at the moment of the peak based on the optical flow as well as the position of the peak.

  Next, in the inheritance (tracking) according to the first embodiment, the peak position tracking unit 44 evaluates the distance between peaks (whether it is included in the predicted position) and the past movement direction (instantaneous movement direction estimation result). Are inherited (tracked) comprehensively based on the evaluation criteria (whether or not match) (step Sb15). That is, at the time of tracking the peak position, the peak position tracking unit 44 executes the tracking process in consideration of not only the peak position but also information on the moving direction at the moment determined from the optical flow of the peak.

  For example, when the movement direction estimation result (positive or negative) at the past moment is the same as the movement direction (positive or negative) of the current tracking candidate peak, it is inherited (tracked). Not necessary. In this way, tracking can be performed with higher accuracy by performing tracking based on the instantaneous moving direction.

  Next, the number-of-peaks estimation unit 37 detects how many persons the tracking object obtained by the tracking process is configured (step Sb16), and stores (adds) the tracking result in the tracking result storage unit 38. (Step Sb17).

  Next, the information processing apparatus 3 determines whether or not an end condition is satisfied, such as an instruction for an end operation by the user (step Sb18). When the end condition is not satisfied (step Sb18-NO), Is processed (step Sb19), the process returns to step Sb10 and the above-described processing is repeated. On the other hand, when the end condition is satisfied (YES in step Sb18), the information processing apparatus 3 ends the process.

  According to the second embodiment described above, by tracking the peak value of the one-dimensional projection value calculated using the scale conversion coefficient obtained from the camera parameter, the calculation cost can be reduced in the tracking process of the moving object. it can. Furthermore, according to the second embodiment of the present invention, tracking can be performed with higher accuracy by performing tracking based on the instantaneous moving direction.

  The embodiment of the present invention has been described in detail with reference to the drawings. However, the specific configuration is not limited to this embodiment, and includes designs and the like that do not depart from the gist of the present invention.

  DESCRIPTION OF SYMBOLS 1 ... Passage number measuring apparatus, 2 ... Camera, 3 ... Information processing apparatus, 30 ... Frame image acquisition part, 31 ... Image | video preservation | save part, 32 ... Foreground detection part, 33, 42 ... One-dimensional projection part, 34 ... Scale conversion coefficient Storage unit 35 ... Peak position detection unit 36,44 ... Peak position tracking unit 37 ... Peak number person estimation unit 38 ... Tracking result storage unit 40 ... Recording medium 41 ... Optical flow calculation unit 43 ... Peak position・ Moving direction detector

Claims (3)

A frame image acquisition step of acquiring frame images in chronological order from video captured by a camera;
A foreground detection step of detecting a foreground pixel that is a pixel corresponding to a moving object from the frame image;
A one-dimensional projection step of projecting the foreground pixels detected in the foreground detection step one-dimensionally based on a scale conversion factor stored in advance by a scale conversion factor storage unit;
A peak position detection step for detecting a peak position of a projection value obtained by the one-dimensional projection step;
A peak position tracking step for tracking the peak position by associating the peak position detected in the previous frame image with the peak position detected in the current frame image;
A moving body tracking method. An optical flow calculating step of calculating an optical flow in the frame image;
A moving direction detection step of estimating based on the optical flow whether the peak position is moving in the one-dimensional direction, and
In the peak position tracking step, the peak position detected in the previous frame image is associated with the peak position detected in the current frame image based on the estimated direction.
The moving body tracking method according to claim 1.   The computer program for making a computer perform the mobile body tracking method of Claim 1 or Claim 2.