Source: https://patents.google.com/patent/JP2014029604A/en
Timestamp: 2020-03-30 18:07:10
Document Index: 340930193

Matched Legal Cases: ['art 20', 'art 30', 'art 20', 'art 40', 'art 50', 'art 40', 'art 21', 'art 22', 'art 23', 'art 24', 'art 30', 'art 40', 'art 41']

JP2014029604A - Moving object recognition system, moving object recognition program, and moving object recognition method - Google Patents
Moving object recognition system, moving object recognition program, and moving object recognition method Download PDF
JP2014029604A
JP2014029604A JP2012169709A JP2012169709A JP2014029604A JP 2014029604 A JP2014029604 A JP 2014029604A JP 2012169709 A JP2012169709 A JP 2012169709A JP 2012169709 A JP2012169709 A JP 2012169709A JP 2014029604 A JP2014029604 A JP 2014029604A
JP2012169709A
JP5944781B2 (en
育郎 佐藤
邦博 後藤
2012-07-31 Application filed by Denso It Laboratory Inc, 株式会社デンソーアイティーラボラトリ, Denso Corp, 株式会社デンソー filed Critical Denso It Laboratory Inc
2012-07-31 Priority to JP2012169709A priority Critical patent/JP5944781B2/en
2014-02-13 Publication of JP2014029604A publication Critical patent/JP2014029604A/en
2016-07-05 Publication of JP5944781B2 publication Critical patent/JP5944781B2/en
PROBLEM TO BE SOLVED: To provide a moving object recognition system in which danger of collision of a moving body is detected by using a monocular camera, not by using a relatively expensive sensor, such as a stereo camera and a distance sensor.SOLUTION: A moving body recognition system 100 includes: a camera 10 installed in a vehicle for photographing a plurality of consecutive single-viewpoint images; a moving body detection part 20 for detecting a moving body in the plurality of images photographed by the camera 10 by using the images; a relative approach angle estimation part 30 for estimating a relative approach angle of the moving body detected by the moving body detection part 20 with respect to the camera 10; a collision danger degree calculation part 40 for calculating, on the basis of a relationship between the relative approach angle and the moving body direction from the camera 10 toward the moving body, a danger degree at which the moving body collides with the vehicle; and a notification part 50 for notifying a driver of the vehicle of the danger in accordance with the danger degree calculated by the collision danger degree calculation part 40.
The present invention relates to a moving object recognition system, a moving object recognition program, and a moving object recognition method for recognizing a moving object using a single viewpoint image.
2. Description of the Related Art Conventionally, a moving body recognition system that recognizes a moving body around a vehicle and performs notification or warning to a driver or automatic braking of the vehicle (hereinafter simply referred to as “notification”) is known. The distance from the host vehicle to the moving body can be directly measured using a distance sensor such as a millimeter wave radar, or can be measured by performing image processing using a stereo camera. By analyzing the measured distances in time series, the moving direction of the moving body and the relative speed with respect to the host vehicle can be obtained. And by calculating | requiring the moving direction of a moving body and the relative speed with respect to the own vehicle, the possibility (risk) that the moving body collides with a vehicle can be calculated | required, and notification can be performed based on the risk. .
As a simpler and cheaper mobile object recognition system, a system that recognizes a mobile object with a monocular camera having only one optical system and an image sensor is also known. This system obtains images of a plurality of single viewpoints arranged in time series by continuously photographing with a monocular camera. A technique described in Patent Document 1 is known as a technique for detecting a moving object from a plurality of single viewpoint images.
In the technique described in Patent Document 1, a group of feature points is grouped for each moving object based on feature points extracted from each of a plurality of single viewpoint images arranged in time series and their motion vectors (optical flow). This grouping is performed according to the convergence of the optical flow to the vanishing point and the variation of the external division ratio. Thereby, even if optical flows calculated inaccurately are mixed, it is possible to reliably detect a moving body.
Japanese Patent No. 4919036
In an in-vehicle mobile object recognition system, it is desirable not to notify the driver of mobile objects that are not in danger of collision, but to notify only of mobile objects that are in danger of collision such as popping out. This is because it is troublesome for the driver to make a notification about any moving body, which may negatively affect driving.
In an in-vehicle mobile object recognition system, the certainty of the result of recognition processing and the risk of collision do not always match. That is, there is a case where there is no danger of a collision despite the high certainty of the result of the recognition process, and vice versa. The information useful for many drivers is not the certainty of the recognition process, but the risk of a collision of the recognized moving body with the host vehicle.
The present invention has been made in view of the above problems, and uses a monocular camera without using a relatively expensive sensor such as a stereo camera or a distance sensor to detect a moving object recognition. The purpose is to provide a system.
A moving body recognition system according to an aspect of the present invention includes a camera that is installed in a vehicle and captures a plurality of continuous single-viewpoint images, and a plurality of images captured by the camera. A moving body detection unit that detects a relative approaching angle estimation unit that estimates a relative approaching angle of the moving body detected by the moving body detection unit with respect to the camera, the relative approaching angle, and the camera And a collision risk calculation unit that calculates a risk that the moving object collides with the vehicle based on a relationship with a moving object direction toward the moving object.
According to this configuration, it is possible to obtain a degree of risk that the moving body collides with the vehicle as information useful for the safe driving of the driver of the vehicle. Further, since the risk level is calculated by a relatively inexpensive configuration such as a monocular camera without using a relatively expensive configuration such as a stereo camera, the cost can be reduced.
In the above moving body recognition system, the collision risk level calculation unit may increase the risk level as the deviation between the relative approach angle and the moving body direction is smaller.
The fact that the relative approach angle and the moving body direction coincide with each other means that the moving body is moving toward the vehicle relatively. Therefore, the smaller the difference between the relative approach angle and the moving body direction is, the more dangerous it is. It can be said that the nature is high. Therefore, according to this configuration, it is possible to suitably calculate the degree of risk that the moving body collides with the vehicle.
In the mobile object recognition system, the collision risk calculation unit sets a danger zone based on the relative approach angle, and calculates the risk based on a probability that the mobile object exists in the danger zone. Good.
According to this configuration, it is possible to accurately calculate the degree of risk that the moving body collides with the vehicle not only based on the relative approach angle of the moving body with respect to the vehicle but also based on the danger zone set based on the angle.
In the above mobile object recognition system, the danger zone is set to an XZ plane when the camera is the origin, the optical axis direction of the camera is the Z direction, the vertical direction is the Y direction, and the horizontal direction is the X direction. The region may extend from the vehicle in the direction of the relative approach angle with the width of the vehicle.
According to this configuration, since the danger zone is set so as to extend in the direction of the relative approach angle from the vehicle with the width of the vehicle, it is possible to calculate the risk of collision with any part of the vehicle.
In the above moving body recognition system, the collision risk calculation unit is a distance estimation unit that estimates an upper limit value of the distance in the Z direction from the camera to the moving body, and is separated from the camera and the camera by a focal length. The ratio of the line segment that enters the danger zone out of the line segment from the camera to the upper limit value of the XZ ray that is an orthogonal projection of the straight line passing through the moving body on the image onto the XZ plane. A risk level calculation unit that calculates the risk level as the risk level.
According to this configuration, since the upper limit of the dangerous zone is provided, even if the distance to the moving body cannot be obtained, the probability that the moving body is in the dangerous zone can be calculated by specifying the moving body direction. .
In the moving object recognition system, the moving object detection unit extracts a plurality of feature points from the image, calculates an optical flow of the extracted feature points, and includes a plurality of feature points. Among them, the extended optical flow may include a grouping unit that groups a plurality of feature points that converge to one vanishing point as a plurality of feature points on the moving body, and the distance estimation unit is grouped The lowest feature point having the lowest height is selected from the plurality of feature points on the moving body, and the intersection of the straight line connecting the lowest feature point and the optical center and the ground on which the vehicle travels is the ground point. The Z coordinate of the ground point may be estimated as the upper limit value of the distance in the Z direction from the camera to the moving body.
The assumption that the distance between a plurality of feature points grouped as a moving body is sufficiently smaller than the distance from the camera is valid in a moving body recognition system that recognizes a moving body around the vehicle. Therefore, according to this structure, the upper limit of the distance of a mobile body can be estimated suitably by simple calculation.
In the above moving body recognition system, the collision risk calculation unit includes a distance estimation unit that estimates a distance in the Z direction from the camera to the moving body, and the image that is separated from the camera and the camera by a focal length. When the point having the estimated distance from the camera on the XZ ray, which is a line segment obtained by orthogonally projecting a straight line passing through the moving body on the XZ plane, is in the danger zone, the point is And a risk level calculation unit that calculates a higher level of risk than when not in the danger zone.
According to this configuration, since the distance to the moving body is estimated, it is possible to determine whether the moving body is in the dangerous zone, not the probability of being in the dangerous zone. A high degree of risk can be calculated. The degree of risk may be classified according to the part of the vehicle that the moving body collides with.
In the moving object recognition system, the moving object detection unit extracts a plurality of feature points from the image, calculates an optical flow of the extracted feature points, and includes a plurality of feature points. Among them, the extended optical flow may include a grouping unit that groups a plurality of feature points that converge to one vanishing point as a plurality of feature points on the moving body, and the distance estimation unit includes the vanishing point The collision time until the moving body collides with the vehicle may be obtained based on the above, and the distance in the Z direction from the camera to the moving body may be estimated based on the collision time and the speed of the vehicle.
Since a moving body that is dangerous for a traveling vehicle is a moving body that crosses the traveling direction of the vehicle (for example, a moving body that passes by the vehicle is not very dangerous), a moving body recognition system that recognizes a moving body around the vehicle. Therefore, it is reasonable to assume that the moving body is moving parallel to the host vehicle (that is, the velocity component in the optical axis direction of the camera of the moving body is 0). Therefore, according to this configuration, the distance of the moving object can be estimated appropriately by simple calculation.
In the above moving body recognition system, the collision risk calculation unit is a distance estimation unit that determines a probability distribution of the distance in the Z direction from the camera to the moving body, and is separated from the camera and the camera by a focal length. Further, the XZ ray, which is a line segment obtained by orthogonally projecting a straight line passing through the moving body on the image to the XZ plane, is based on the integrated value of the probability density function of the probability distribution in the distance range in the danger zone. A risk level calculation unit that calculates the risk level may be provided.
According to this configuration, since the distance is not estimated as a single value but is obtained as a probability distribution, the degree of risk is suitably determined according to the ratio of the integral value of the probability density function of the probability distribution over the danger zone. Can be calculated.
In the moving object recognition system, the moving object detection unit extracts a plurality of feature points from the image, calculates an optical flow of the extracted feature points, and includes a plurality of feature points. Among them, the extended optical flow may include a grouping unit that groups a plurality of feature points that converge to one vanishing point as a plurality of feature points on the moving body, and the distance estimation unit includes the vanishing point The collision time until the mobile body collides with the vehicle may be obtained based on the above, and the probability distribution of the distance may be determined according to a predetermined normal distribution based on the collision time.
In a mobile object recognition system that recognizes a mobile object around a vehicle, the assumption that the probability distribution of distance is a normal distribution is valid. According to this configuration, the distance of the mobile object is preferably calculated by simple calculation. Probability distribution can be determined.
In the above moving body recognition system, the collision risk calculation unit estimates an upper limit value of the distance in the Z direction from the camera to the moving body, and the distance in the Z direction from the camera to the moving body. A distance estimation unit for determining a probability distribution of the moving object on the image separated from the camera by a focal length with respect to an integral value of a probability density function of the probability distribution in a distance range up to the upper limit value A risk level for calculating the risk level based on the ratio of the integral value of the probability density function of the probability distribution in the distance range where the XZ ray, which is a line segment orthogonally projected to the XZ plane, is in the risk zone And a calculation unit.
According to this configuration, since the distance is not estimated as one value but is obtained as a probability distribution and a portion larger than the estimated upper limit value of the random variable is rounded down, the probability density function in the remaining range is reduced. The degree of danger can be suitably calculated in accordance with the ratio of the integral value of the probability density function applied to the danger zone in the integral value.
In the moving object recognition system, the moving object detection unit extracts a plurality of feature points from the image, calculates an optical flow of the extracted feature points, and includes a plurality of feature points. Among them, the extended optical flow may include a grouping unit that groups a plurality of feature points that converge to one vanishing point as a plurality of feature points on the moving body, and the distance estimation unit is grouped The lowest feature point having the lowest height is selected from the plurality of feature points on the moving body, and the intersection of the straight line connecting the lowest feature point and the optical center and the ground on which the vehicle travels is the ground point. The Z coordinate of the ground point is estimated as the upper limit value of the distance in the Z direction from the camera to the moving body, and the moving body collides with the vehicle based on the vanishing point. Collision time in the determined, on the basis of the contact time, may determine the probability distribution of the distance in accordance with a predetermined normal distribution.
This assumption is that the assumption that the distance between multiple feature points grouped as a moving object is sufficiently smaller than the distance from the camera is valid in a moving object recognition system that recognizes moving objects around the vehicle. Accordingly, the upper limit of the distance of the moving object can be estimated appropriately by simple calculation. In addition, in the mobile object recognition system that recognizes mobile objects around the vehicle, the assumption that the probability distribution of distance is a normal distribution has validity, and according to this configuration, the mobile object can be suitably obtained by simple calculation. The probability distribution of distance can be determined.
In the above mobile object recognition system, the collision risk calculation unit may divide the danger zone into a plurality of levels and set in stages.
According to this configuration, it is possible to calculate the degree of risk according to the magnitude of the influence when each part of the vehicle collides.
A moving body recognition system according to another aspect of the present invention includes a camera that is installed in a vehicle and captures a plurality of continuous single-viewpoint images, and a plurality of images captured by the camera. A feature point is extracted, an optical flow of the extracted feature points is generated, and among the plurality of feature points, a plurality of feature points where the extended optical flow converges to one vanishing point is grouped The collision risk calculation unit includes: a moving object detection unit that detects the grouped feature points as a moving object; and a collision risk calculation unit that calculates a risk that the moving object collides with the vehicle. The risk is that the risk of the moving object that is close to the vanishing point in the image is higher than the risk of the moving object that is far from the vanishing point in the image. In, it calculates the risk.
Also with this configuration, it is possible to obtain the degree of risk that the moving body collides with the vehicle as information useful for the safe driving of the driver of the vehicle. Further, since the risk level is calculated by a relatively inexpensive configuration such as a monocular camera without using a relatively expensive configuration such as a stereo camera, the cost can be reduced. Furthermore, based on the distance between the vanishing point and the moving object on the image, the degree of risk can be calculated by simple calculation.
In the above moving body recognition system, the collision risk degree calculation unit may be any one of a plurality of feature points on the moving body, or a point obtained from a plurality of feature points on the moving body. As a representative point, the risk of the representative point colliding with the vehicle may be calculated.
According to this configuration, a representative point appropriate for the position of the moving body can be specified.
The moving body recognition system may further include a notifying unit that notifies the driver of the vehicle of the danger according to the risk calculated by the collision risk calculating unit.
According to this configuration, the driver can be alerted according to the degree of risk that the moving body collides with the vehicle.
In the above moving body recognition system, the collision risk calculation unit may correct the risk based on a collision time until the moving body collides with the vehicle.
According to this configuration, it is possible to obtain a more appropriate degree of risk in consideration of the collision time.
A moving body recognition program according to an aspect of the present invention uses a plurality of images captured by a camera that is installed in a vehicle and captures a plurality of continuous single-viewpoint images. A moving body detection unit to detect, a relative approach angle estimation unit to estimate a relative approach angle of the mobile body detected by the mobile body detection unit to the camera, and the relative approach angle and the movement from the camera Based on the relationship with the moving body direction toward the body, the collision risk degree calculation unit that calculates the risk that the moving body collides with the vehicle is caused to function.
Also with this configuration, it is possible to obtain the degree of risk that the moving body collides with the vehicle as information useful for the safe driving of the driver of the vehicle. Further, since the risk level is calculated by a relatively inexpensive configuration such as a monocular camera without using a relatively expensive configuration such as a stereo camera, the cost can be reduced.
According to another aspect of the present invention, there is provided a moving body recognition program that uses a plurality of images captured by a camera that is installed in a vehicle and captures a plurality of continuous single-viewpoint images. By extracting feature points, generating an optical flow of the extracted feature points, and grouping a plurality of feature points where the extended optical flow converges to one vanishing point among the plurality of feature points. A moving object detection unit that detects the grouped feature points as a moving object, and a collision risk calculation unit that calculates a risk that the moving object will collide with the vehicle, the vanishing point in the image The risk level is set so that the risk level of the moving object that is close to the distance between the moving object is higher than the risk level of the moving object that is far from the vanishing point in the image. To function as a collision risk calculation unit for output.
A moving body recognition method according to an aspect of the present invention includes an imaging step of capturing a plurality of continuous single-viewpoint images with a camera installed in a vehicle, and a plurality of images captured by the camera. A mobile body detection step for detecting a mobile body in the mobile body; a relative approach angle estimation step for estimating a relative approach angle of the mobile body detected in the mobile body detection step with respect to the camera; and the relative approach angle; And a collision risk calculating step of calculating a risk of collision of the moving body with the vehicle based on a relationship with a moving body direction from the camera toward the moving body.
According to another aspect of the present invention, there is provided a moving body recognition method using a camera installed in a vehicle, a shooting step of shooting a plurality of continuous single-viewpoint images, and a plurality of images shot by the camera. A plurality of feature points are extracted from the image, an optical flow of the extracted feature points is generated, and among the plurality of feature points, a plurality of feature points where the extended optical flow converges to one vanishing point A moving body detecting step of detecting the plurality of grouped feature points as a moving body, and a collision risk calculating step of calculating a risk of collision of the moving body with the vehicle, The collision risk calculation step includes the movement of the moving object having a short distance from the vanishing point in the image, the risk of the moving body being close to the vanishing point in the image. Said to be higher than the risk of, it calculates the risk.
ADVANTAGE OF THE INVENTION According to this invention, the risk that a mobile body collides with a vehicle can be obtained as information useful for the safe driving | operation of the driver of a vehicle. Further, since the risk level is calculated by a relatively inexpensive configuration such as a monocular camera without using a relatively expensive configuration such as a stereo camera, the cost can be reduced.
The block diagram which shows the structure of the mobile body recognition system in embodiment of this invention. (A) The figure which shows the example of the ground speed for demonstrating the relative approach angle in embodiment of this invention (b) The figure which shows the relative approach angle in embodiment of this invention The figure explaining the definition of the coordinate in embodiment of this invention The figure explaining the connection optical flow in embodiment of this invention The figure which shows the distance which the point cloud of the three-dimensional space in the embodiment of this invention moves in translation in unit time The figure explaining the vanishing point in embodiment of this invention (A) Graph showing the cost function of robust estimation in the embodiment of the present invention (b) Graph showing the influence function of robust estimation in the embodiment of the present invention The figure which shows the example of the image which an outlier tends to generate | occur | produce in embodiment of this invention (A) The figure explaining the definition of the relative approach angle (when θ <0) in the embodiment of the present invention (b) The definition of the relative approach angle (when θ> 0) according to the embodiment of the present invention Figure explaining The figure which shows the relationship between the relative approach angle and vanishing point in embodiment of this invention The figure explaining the ground point of the 1st example of the distance estimation method in embodiment of this invention The figure explaining calculation of the upper limit of the distance of the 1st example of the distance estimation method in embodiment of this invention The figure for demonstrating calculation of the risk based on the estimated value of the upper limit of the distance in embodiment of this invention The figure for demonstrating calculation of the risk based on the estimated value of the distance in embodiment of this invention The figure for demonstrating the modification of the calculation of the risk based on the estimated value of the distance in embodiment of this invention The figure for demonstrating calculation of the risk based on the probability density function of the distance in embodiment of this invention The figure for demonstrating calculation of the risk based on the upper limit of the estimated distance and distance distribution in embodiment of this invention The figure for demonstrating calculation of the risk degree which considered TTC in embodiment of this invention
Hereinafter, a mobile object recognition system according to an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a block diagram showing a configuration of a mobile object recognition system according to an embodiment of the present invention. The moving body recognition system 100 of this embodiment is a system that calculates the degree of danger that a moving body will collide with the host vehicle and notifies the driver. The moving body recognition system 100 includes a camera 10, a moving body detection unit 20, a relative approach angle estimation unit 30, a collision risk calculation unit 40, and a notification unit 50. The configuration including the moving object detection unit 20, the relative approach angle estimation unit 30, and the collision risk calculation unit 40 is realized by the computer executing the moving object recognition program according to the embodiment of the present invention. The moving body recognition program may be stored in a storage medium, read from the storage medium by a computer, and executed by the computer.
First, the outline of the mobile object recognition system 100 will be described. The camera 10 is mounted on the vehicle, and continuously captures the periphery of the vehicle at a constant cycle while the vehicle is running, generates an image, and outputs the image to the moving body detection unit 20. The moving body detection unit 20 uses a plurality of images sequentially input from the camera 10 to detect a moving body from these images. Since the own vehicle (that is, the camera 10) is moving, the position of the object that is stationary with respect to the ground also changes every moment. However, the moving object detection unit 20 has a plurality of such images. From this, an object moving relative to the ground is detected as a moving object.
The relative approach angle estimation unit 30 estimates a relative movement direction of the detected moving body with respect to the vehicle as a relative approach angle. FIG. 2 is a diagram illustrating a relative approach angle in the embodiment of the present invention. Now, as shown in FIG. 2 (a), the vehicle C has straight at a speed v c forward, the moving body O is moving at a velocity v o in a direction perpendicular to the traveling direction of the vehicle at its forward Then, as shown in FIG. 2B, the relative approach angle θ is obtained as an angle formed by v o −v c with the forward direction of the vehicle. The relative approach angle estimation unit 30 estimates the relative approach angle θ using the image and the focal length of the camera 10.
The collision risk degree calculation unit 40 estimates the distance from the host vehicle to the moving body detected by the moving body detection unit 20, and the estimated distance and the relative approach angle estimated by the relative approach angle estimation unit 30. Based on the above, the risk of collision is calculated. The notification unit 50 notifies the driver based on the risk calculated by the collision risk calculation unit 40. Details will be described below.
The camera 10 is mounted on a vehicle and photographs the periphery of the vehicle. In particular, since the moving body recognition system 100 according to the present embodiment calculates and notifies the danger of a collision with the host vehicle, the camera 10 is installed in the vehicle so as to capture the traveling direction of the host vehicle. Is done. The camera 10 may be installed, for example, on the back side (front side) of the rearview mirror so as to photograph the front of the vehicle. Since the vehicle can also move backward, the camera 10 may be installed, for example, near the rear license plate so that the camera 10 captures the rear of the vehicle. The camera 10 is a monocular camera provided with a single optical system and an image sensor. The camera 10 continuously captures images at a predetermined cycle (for example, every 1/30 seconds) and outputs an image signal.
The moving object detection unit 20 groups the feature points of the same moving object from the coordinates of the feature points in the image and the motion vector (optical flow) information based on the rigid body model that performs linear motion and the pinhole camera model ( Clustering) (see JP 2011-81613 A for details). Here, the optical flow is a trajectory obtained by tracking the same image patch (for example, a small area of 5 pixels square) in a moving image or the like. The moving body detection unit 20 also performs a process of removing components resulting from the rotational movement of the camera 10 from the optical flow. The moving object detection unit 20 includes a connected optical flow calculation unit 21, a rotational movement amount / vanishing point estimation unit 22, a background point removal unit 23, and a grouping unit 24.
First, the coordinates used in the following calculation are defined. FIG. 3 is a diagram for explaining the definition of coordinates in the embodiment of the present invention. When the road surface is flat, it is assumed that the optical axis of the camera 10 and the road surface are parallel. The optical axis of the camera 10 is the Z axis, the vertically downward direction is the Y axis, and the X axis is defined by the right hand coordinate system. The origin (0, 0, 0) is the optical center of the camera 10. In this embodiment, it is assumed that the optical axis of the camera 10 is parallel to the road surface. However, even when the optical axis of the camera 10 and the road surface are not parallel, it can be easily generalized by introducing an appropriate rotation matrix. is there. Considering a perspective projection model, the image coordinates are given by (x, y) = (fX / Z, fY / Z). Here, f is the focal length of the camera 10 and is known.
The connected optical flow calculation unit 21 extracts feature points corresponding to a plurality of images from a plurality of images obtained by the camera 10, and calculates an optical flow for each feature point (J. shi and C. Tomasi, “Good features to track,” IEEE CVPR, pp. 593-600, 1994). In this embodiment, the LK method is used as an optical flow calculation algorithm (BD Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision,” IJCAI, pp. 674-679, 1981. reference).
In calculating the optical flow, it is desirable that there are few outliers. The outlier generally refers to an unexpected calculation result, and the optical flow outlier particularly refers to a trajectory tracked in error. Although the technique of the above-mentioned Patent Document 1 is robust against an optical flow outlier, it is still rare that a plurality of optical flows are outliers and have the same vanishing point and external division ratio with an allowable error. It may have in the range of. In this case, since an outlier is detected, the risk level is erroneously calculated. In this embodiment, an outlier removal method with a low calculation amount is provided.
In order to improve the accuracy of the optical flow, in the present embodiment, instead of using the optical flow between two adjacent frames, a concatenation of a plurality of optical flows between adjacent frames is used. In this specification, an optical flow between two adjacent frames, that is, a vector from a feature point of one frame to the same (corresponding) feature point in the next frame of the frame is referred to as “single optical flow. And the same as the feature point in the frame obtained by connecting a plurality of single optical flows, that is, the feature point in one frame at a predetermined number of frames of 1 or more from that frame (corresponding The vector to the feature point is called “connected optical flow”.
FIG. 4 is a diagram for explaining a connection optical flow in the embodiment of the present invention. In FIG. 4, the left column is a sequence in which images continuously acquired by the camera 10 are arranged in chronological order from the top, the central column shows a single optical flow, and the right column is A consolidated optical flow is shown in which three single optical flows are connected. The connected optical flow calculation unit 21 connects single optical flows for a plurality of frames of the same (corresponding) feature points, and calculates a connected optical flow.
As shown in FIG. 4, a single optical flow is generated by connecting the same feature points between two adjacent frames, and a connected optical flow is generated by connecting a plurality of single optical flows. In the example of FIG. 4, a connected optical flow is generated by connecting three (n = 3) optical flows.
The rotational movement amount / vanishing point estimation unit 22 estimates the vanishing point from the connected optical flow. In general, when a three-dimensional point cloud translates at a constant speed, the trajectory of a two-dimensional point cloud that is a perspective projection of these point clouds has a feature that the extension lines intersect at one point. . This intersection is the vanishing point. In this specification, the term vanishing point is used to mean the intersection of the extension lines of the locus of points on the image plane. When there are a plurality of moving objects, vanishing points are estimated for each moving object.
FIG. 5 is a diagram showing the distance (the velocity of the point group in the three-dimensional space) that the point group (point on the moving body) in the three-dimensional space moves in translation in unit time. When this velocity V is (ΔX, ΔY, ΔZ), the image coordinates x (t) = f (X + tΔX) / (Z + tΔZ) projected at time t, y (t) = f (Y + tΔY) / (Z + tΔZ) Converges to (fΔX / ΔZ, fΔY / ΔZ) in the limit of t → −∞ or t → ∞. Therefore, the two-dimensional coordinates of the vanishing point are given as (fΔX / ΔZ, fΔY / ΔZ). Note that the point cloud that converges at t → −∞ is a point cloud that moves away from the camera, and the point cloud that converges at t → ∞ is a point cloud that approaches the camera.
FIG. 6 is a diagram illustrating vanishing points. In the example of FIG. 6, the extension lines of the connected optical flows COF 1 to 6 of the feature points FP 1 to 6 intersect at one point, and this point becomes the vanishing point VP. The rotational movement amount / vanishing point estimation unit 22 extends the connection optical flow of each feature point, searches for a point where a plurality of connection optical flows intersect, and estimates it as a vanishing point. Considering that the optical flow includes the outlier, the optical flow of the moving object, and the optical flow of the background object, it is appropriate to apply the robust estimation to the vanishing point estimation. Robust estimation refers to parameter estimation that is robust against outliers. In this embodiment, M estimation (see P. J. Huber and E. M. Ronchetti, “Robust Statistice, 2nd Edition,” Wiley Interscience) is applied as robust estimation.
If the above technique cannot be applied because, for example, the calculation capability is limited, the rotational movement amount / vanishing point estimation unit 22 may obtain the vanishing point of the connected optical flow by the following simple process. Good. In this method, the rotational movement amount / vanishing point estimation unit 22 records the position of the horizon in the image of the camera 10 in advance, and the intersection of the horizon and the connected optical flow is used as the vanishing point of the locus of this point. . This method is based on the fact that when the moving object and the camera 10 are moving parallel to the flat ground, the vanishing point of the moving object's trajectory exists on the horizon. However, if the slope of the connected optical flow is small (when it is almost parallel to the horizon), the error of the connected optical flow can greatly change the value of the vanishing point. If the range is too large, this point is excluded from the detection candidates as an exceptional process.
Further, the rotational movement amount / vanishing point estimation unit 22 estimates an optical flow component caused by the rotational movement of the camera 10 and removes it from the connected optical flow. Now, a three-dimensional point (X, V, X , V y , V z ) and a three-dimensional point (X, V) projected by a camera having a rotational movement amount Ω = (Ω x , Ω y , Ω z ). If the optical flow of Y, Z) is v = (v x , v y ), this optical flow v is expressed by the following equation (1).
Here, x = fX / Z, y = fY / Z, and v x r and v y r are components of an optical flow consisting of a first-order term of the rotation amount of the camera 10 given by the following equation (2). It is.
By replacing p F = (x F , y F ) = (fV x / V z , fV y / V z ) from the expression (2) and eliminating the depth components Z and V z , the following expression (3) is obtained: can get.
Here, R (p F , Ω) is an error function in the parameter space, and takes 0 in the ideal state. That is, the expression (3) means that when the optical flow of the stationary point from which the rotation component is removed is extended, it intersects at one point on the image (that is, FOE: Focus Of Expansion).
Since the outflow value of the optical flow and the point of the moving body are generally considered to have a large R value, in this embodiment, the rotational movement amount / vanishing point estimation unit 22 performs the M estimation of Expression (4). Is used.
Here, ρ (R) is a function having a minimum value of 0 and symmetric with respect to R = 0. The degree of robustness against outliers is characterized by the influence function ψ (R) = ∂ρ / ∂R.
FIG. 7A is a graph showing a cost function of robust estimation, and FIG. 7B is a diagram showing an influence function of robust estimation. 7A and 7B, the broken line indicates the L2 norm, and the solid line indicates the Couchy function. In this embodiment, the Cauchy influence function defined by FIG. 7 and the following equation (5) is used.
Since the influence function of Couchy is not a monotonically increasing function at R> 0 but starts to decrease at the extreme value, the influence on the input solution having a large error can be suppressed to a low level. The constant C in equation (5) is C = 2.385. This value is set so as to give 95% efficiency of the least squares method of Gaussian distribution with an average of 0.
The rotational movement amount / vanishing point estimation unit 22 uses an iteratively reweighted least squares (IRLS) method as a solution for M estimation. This is a method in which the cost function of Equation (4) is transformed into a weighted error sum of squares, and the least square method and weight update are alternately repeated until the solution converges. This weight is given by ω = ψ (R) / R using an influence function. The IRLS algorithm is shown below.
The denominator 1.48 mad (R) in the rescaling in step 3) is set to be equal to the standard deviation when R follows a normal distribution. The reason for using the median absolute deviation (mad) instead of the standard deviation is to suppress the fluctuation of the scale due to the mixture of outliers. Since the error is expressed in the form of the product of the FOE coordinates and the rotational movement amount, step 4) is a nonlinear least square method. In the present embodiment, the rotational movement amount / vanishing point estimation unit 22 obtains a solution by the Newton method. The rotational movement amount / vanishing point estimation unit 22 removes the component resulting from the obtained rotational movement amount from the connected optical flow.
The background point removal unit 23 removes the connected optical flow as an object that does not move with respect to the background, that is, the ground, when a straight line obtained by extending the connected optical flow passes through the vanishing point of the background within the allowable error range. That is, since the moving body that moves relative to the ground has a vanishing point at a position different from the background, in order to detect a feature point group having such a vanishing point as a moving body, the background point removing unit 23 The feature point having the vanishing point of the background and its connected optical flow are removed.
Next, the grouping unit 24 will be described. The connected optical flow generated by the connected optical flow calculation unit 21, the component resulting from the rotational movement amount removed by the rotational movement amount / vanishing point estimation unit 22, and removed as the background by the background removal unit 23 is Either an outlier or an optical flow of feature points on a correctly tracked moving object. The grouping unit 24 performs grouping of feature points and the connected optical flows while removing outliers using the connected optical flows.
The grouping unit 24 robustly estimates the vanishing point again for the remaining feature points and the connected optical flows. First, the grouping unit 24 groups the characteristic points that pass through this vanishing point within the allowable error range and the connected optical flow into a temporary group, and groups outliers from the temporary group. Remove and finally finalize the group. First, the grouping unit 24 determines and removes the outlier based on the straightness of the connected optical flow, and further determines and removes the outlier from the degree of similarity of TTC calculated from the connected optical flow. Hereinafter, it demonstrates in order.
(Outlier removal based on straightness)
If a feature point is tracked correctly, the locus will be a straight line as long as the point moves in a uniform linear motion in a three-dimensional space. In general, the constant velocity linear motion model is a good approximation when the position of a moving object is observed at sufficiently short time intervals. Therefore, it is effective for removing outliers to determine whether or not a plurality of single optical flows are arranged linearly.
FIG. 8 is a diagram illustrating an example of an image in which an outlier is likely to occur. As shown in Fig. 8, if there are trees in the image and the image contains many similar leaves and branches, that is, if the image contains multiple similar patterns, multiple frames are supported. When a feature point is detected, points FP t1 , FP t2 , FP t3 , and FP t4 that do not originally correspond are detected as corresponding feature points. Such feature points and the optical flow connecting them are outliers. At this time, when the connected optical flow COF of the feature points FP t1 , FP t2 , FP t3 , and FP t4 detected as the outliers is obtained, as shown in FIG. 8, the deviation between the connected optical flow COF and each single optical flow is obtained. Becomes larger.
In order to determine and remove such outliers, the grouping unit 24 extracts a component orthogonal to the connected optical flow of each single optical flow, and quantifies the degree of straightness from the extracted component, and the value and threshold value. The outlier is determined by comparing with. Specifically, the grouping unit 24 determines, for each feature point, whether or not each single optical flow constituting the connected optical flow is linear in the following manner.
Now, singly optical flows constituting the n-linked optical flow are time-sequentially set (V x (i), V y (i)), i = 1, 2,..., N, and the n-linked optical flow is (V x ( 1: n) and V y (1: n)). First, the grouping unit 24 calculates a unit vector orthogonal to the connected optical flow. Specifically, this unit vector is given by (V y (1: n), −V x (1: n)) / sqrt (V x (1: n) 2 + V y (1: n) 2 ). .
Next, the absolute value of the inner product of each unit optical flow constituting the n-connected optical flow and this unit vector is calculated, the sum of n values is taken, and this value is used as a linearity index. The grouping unit 24 compares the linearity index with a predetermined threshold value, and excludes feature points having a linearity index of the connected optical flow larger than the threshold value and the connected optical flow from the temporary group.
(Outlier removal based on TTC similarity)
The grouping unit 24 calculates a collision time (TTC: Time To Collision) based on the connection optical flow of each feature point. TTC refers to the time until a point on the image plane reaches the image plane when a three-dimensional point is approaching the camera. In calculating the collision time, the image plane has an infinite extent. Also, TTC when the point moves away from the camera takes a negative value.
Now, the two-dimensional vector representing a consolidated optical flow and u, respectively the x and y components, u x, When u y, satisfies the u 2 = u x 2 + u y 2, u x is the three-dimensional It is expressed by the following equation (6) by the coordinates X, Z and the speeds ΔX, ΔZ.
Here, f is the focal length of the camera 10. Considering that the term of the square of velocity is an order that can be ignored, the following equation (7) is obtained.
Here, the x-coordinate x ∞ of the vanishing point is a position that converges when the time goes back infinitely (when the point is approaching), and is expressed as the following equation (8).
Here, when TTC is expressed as ΔT, ΔT is given by Z / (− ΔZ) (ΔZ <0 when the feature points approach). Since the same derivation can be performed for the y component u y of the optical flow, the following equation (9) is obtained.
When the equations (7) and (9) are solved simultaneously, the following equation (10) is obtained.
Here, p is the image coordinate of the feature point, p ∞ is the image coordinate of the vanishing point of the locus of the point, and “·” is an inner product operator.
The grouping unit 24 calculates the collision time ΔT n from the n-linked optical flow (V x (1: n), V y (1: n)) of each feature point using the equation (10). The grouping unit 24 calculates ΔT n−1 , ΔT n−2 ,... Of the same feature point by the same method as ΔT n . When a feature point is correctly tracked, ΔT n , ΔT n−1 ,... Are equal to each other as long as the point is in a constant velocity linear motion in a three-dimensional space. As described above, when a moving body is observed at a sufficiently short time interval, the constant velocity linear motion model is a good approximation, so that ΔT n , ΔT n−1 ,... Can be said to be similar to each other. When the similarity of ΔT n , ΔT n−1 ,... Is broken, it is a result of the point being mistakenly tracked and can be determined to be an outlier.
The grouping unit 24 obtains the similarity of the collision times ΔT n , ΔT n−1 ,... For each of the feature point groups constituting the n-linked optical flow, compares it with a predetermined threshold, Are also excluded from the tentative group. Specifically, the grouping unit 24 quantifies the deviation of TTC as D j = | ΔT nj −ΔT n | (j = 1,..., N−1), and j satisfies D j > D th. If there is one, the feature point and its connected optical flow are excluded from the group. Here, D th is a predetermined threshold value.
The grouping unit 24 performs grouping of connected optical flows by removing the outliers by the method based on the straightness and the method based on the TTC similarity. A feature point of a plurality of connected optical flows grouped into one group is a feature point of one moving body. The grouping unit 24 detects moving bodies by grouping feature points in this way. Note that the grouping unit 24 may remove the outliers by only one of the method based on the straightness and the method based on the TTC similarity.
Next, the relative approach angle estimation unit 30 will be described. The relative approach angle estimation unit 30 estimates the relative approach angle of the moving body with respect to the vehicle with respect to the moving body detected by the moving body detection unit 20. FIG. 9 is a diagram for explaining the definition of the relative approach angle. FIG. 9A shows a case where θ <0, and FIG. 9B shows a case where θ> 0. As shown in FIGS. 9A and 9B, when a three-dimensional point moves from P 0 to P 1 , an angle given by the following equation (11) is defined as a relative approach angle.
When there is a point cloud that shares the same vanishing point within the allowable error range, the relative approach angle of this point cloud can be estimated.
FIG. 10 is a diagram illustrating the relationship between the relative approach angle and the vanishing point. When there are a plurality of three-dimensional points Pa and Pb of a moving body that translates at the same speed, the trajectory of these points after perspective projection has a vanishing point. Now, considering the vector V = (fΔX / ΔZ, fΔY / ΔZ, f) from the optical center to the vanishing point VP, this vector V is the velocity vector va = vb = (ΔX, ΔY, ΔZ of the points Pa and Pb. ) Times a constant (f / ΔZ times). Therefore, if an image plane perpendicular to the Z direction is set away from the optical center by the focal length f in the Z direction, the vector V moving from the optical center to the vanishing point VP on the image moves in translation at the same speed. The velocity vectors va and vb of the plurality of points Pa and Pb on the three-dimensional body are parallel to each other. From this, the relative approach angle is expressed by the following equation (12).
Here, x∞ = fΔX / ΔZ, s is set to +1 when a two-dimensional point moves away from the vanishing point (ΔZ <0), and set to −1 when approaching (ΔZ> 0). The relative approach angle calculation estimation unit 30 calculates an estimated value of the relative approach angle of the mobile body detected by the mobile body detection unit 20 with respect to the vehicle by Expression (12).
Next, the collision risk calculation unit 40 will be described. The collision risk calculation unit 40 calculates the risk of collision between the moving object and the host vehicle based on the relative approach angle, TTC, and distance estimation value. The collision risk calculation unit 40 includes a distance estimation unit 41 and a risk calculation unit 42. The distance estimation unit 41 estimates the distance in the Z direction from the host vehicle to the moving body (hereinafter simply referred to as “distance”). Based on the distance estimated by the distance estimator 41, the risk calculator 42 calculates the risk that the moving object will collide with the host vehicle.
There are a plurality of methods described below for estimating the distance by the distance estimating unit 41. The distance estimation unit 41 may estimate the distance by one of the following estimation methods, or may estimate each by a plurality of estimation methods. Since the estimated distance is used for risk level calculation in the risk level calculation unit 42, the distance estimation unit 41 estimates the distance necessary for the risk level calculation method in the risk level calculation unit 42. Each will be described below.
(First example of distance estimation method: method of calculating the upper limit of distance)
In the first example, the distance estimation unit 41 estimates the upper limit of the distance to the moving body. FIG. 11 is a diagram for explaining ground points in the first example of the distance estimation method. FIG. 12 is a diagram for explaining the calculation of the upper limit value of the distance in the first example of the distance estimation method. As shown in FIG. 11, the distance estimation unit 41 selects a feature point having the largest y-coordinate value (hereinafter referred to as “lowermost feature point”) from the grouped feature point group, and determines the lowest feature point. The coordinates of the intersection of the straight line connecting the optical centers and the ground G (hereinafter referred to as “ground point”) GP are calculated.
As shown in FIG. 11, as long as the feature point group detected as a point on the moving body is above the ground, the Z coordinate of the three-dimensional point indicated by the lowest feature point is the ground point GP. Smaller than the Z coordinate. Assuming that the distance between the feature points of the grouped feature point group is sufficiently smaller than the distance from the camera 10, the Z coordinate of the ground point GP is regarded as the upper limit value of the Z coordinate of the feature point group. be able to. The Z coordinate of the ground point GP, that is, the upper limit value D1 of the distance is given by D1 = Hc * f / y_max as shown in FIG. Here, Hc is the height of the camera 10 from the ground, y_max is the y coordinate of the lowest feature point, and f is the focal length, that is, the distance from the optical center of the camera to the image plane IP. This method is applicable only when y_max is positive. The distance estimation unit 41 calculates Hc * f / y_max as the upper limit value D1 of the distance.
(Second example of distance estimation method: method of directly estimating distance)
In the second example, the distance estimation unit 41 specifies one representative point representing the moving body, and directly estimates the distance using the translational movement speed of the host vehicle and the collision time TTC of the representative point. The distance estimation unit 41 selects any one of a plurality of feature points grouped as a moving body as a representative point. Since the TTC of the feature point selected as the representative point is obtained by the moving object detection unit 20, this is used. When TTC is ΔT, ΔT = Z / (− ΔZ). Assuming that the moving body is translating perpendicular to the traveling direction of the host vehicle, ΔZ = −V c . Here, V c is the translational movement speed of the host vehicle, and the distance estimation unit 41 acquires this V c by a configuration not shown. The distance estimation unit 41 estimates Z, that is, the distance from the host vehicle to the moving body by Z = ΔTV c .
(Third example of distance estimation method: method of determining probability distribution of distance)
In the third example, the distance estimation unit 41 identifies one representative point that represents the moving object, and determines the probability distribution of the distance using the collision time TTC of the representative point. The distance estimation unit 41 selects any one of a plurality of feature points grouped as a moving body as a representative point. Since the TTC of the feature point selected as the representative point is obtained by the moving object detection unit 20, this is used. In the third example, the distance estimation unit 41 assumes a probability distribution of the velocity of the moving body in the Z direction. An object that moves with a relative approach angle different from the relative approach angle of the background point has a non-zero angle with respect to the translational movement direction of the host vehicle. It is experientially appropriate to set the probability distribution of the velocity in the Z direction of these moving bodies as a normal distribution with an average of zero.
The distance estimation unit 41 stores in advance the average −V of the speed ΔZ in the Z direction of the representative point of the moving object, and the standard deviation σ also stores a fixed value in advance, for example, as 3 km / h. The distribution of the probability of the speed ΔZ in the Z direction of the representative point of the moving body follows a normal distribution with an average of −V and a standard deviation σ. If the TTC of the representative point is expressed as ΔT, the distance of the representative point is Z = ΔT (−ΔZ). Therefore, the probability distribution of the distance Z of the representative point is a normal distribution with an average ΔT (−V) and a standard deviation ΔTσ. Will follow. The distance estimation unit 41 determines the probability distribution of the representative point distance Z as a normal distribution with an average ΔT (−V) and a standard deviation ΔTσ.
The risk level calculation unit 42 defines a risk zone based on the relative approach angle θ estimated by the relative approach angle estimation unit 30, and then calculates the risk level at which the moving object collides with the host vehicle. The danger zone is an area in the XZ plane, and refers to a range in which a point collides with the vehicle when a point on the XZ plane approaches the host vehicle with a certain relative approach angle. However, the portion of the XZ plane occupied by the host vehicle is a non-hazardous zone. The risk calculation unit 42 calculates the risk by a method according to the distance estimation method in the distance estimation unit 41. Hereinafter, it demonstrates in order.
(First example of risk level calculation: risk level calculation based on estimated upper limit of distance)
FIG. 13 is a diagram for explaining the calculation of the degree of risk based on the estimated upper limit of distance. The danger zone DZ extends in the direction inclined by the relative approach angle θ from the traveling direction of the vehicle C (Z-axis direction) with the width of the vehicle C in a direction perpendicular to the direction, and the vehicle is determined from the estimated upper limit D1 of the distance. This is a region close to C. The risk level calculation unit 42 sets a danger zone tilted to the right by the relative approach angle θ when the moving body is on the right side of the vehicle C, and relative to the left side when the moving body is on the left side of the vehicle C. A danger zone that is tilted by the target approach angle θ is set. Further, a straight projection of the straight line passing through the optical center and a certain point on the image onto the XZ plane is called an XZ ray. In other words, the XZ ray is a straight line passing through the origin, the inclination of which is given by X / Z = x / f. FIG. 13 shows an XZ ray (1) and an XZ ray (2).
The risk level calculation unit 42 identifies one representative point that represents the moving object, and calculates the risk level that the representative point collides with the vehicle using the XZ example of the representative point and the danger zone DZ. Specifically, the risk level calculation unit 42 calculates the ratio of the line segment that enters the danger zone DZ in the line segment between the origin and the upper limit value D1 of the representative point XZ ray, and uses this value as the risk level. Degree. In the example of FIG. 13, the risk level of the XZ ray (1) is L1 / (L1 + L2), and since all the XZ rays (2) are included in the risk zone, the risk level is 100% (maximum). .
As is apparent from FIG. 13, in this example, in the XZ plane, the risk increases as the deviation between the direction of the relative approach angle θ and the direction of the position vector of the representative point decreases (the degree of coincidence increases). Like that. In other words, this means that the risk level increases as the vanishing point and the representative point are closer in the image.
The risk level calculation unit 42 may calculate the risk level as a level divided into a plurality of stages by rounding the ratio to a discrete value. In this case, the higher the ratio, the more dangerous the level.
(Second example of risk calculation: calculation of risk based on estimated distance)
FIG. 14 is a diagram for explaining calculation of the degree of risk based on the estimated value of distance. As in the above calculation example, the risk level calculation unit 42 extends in a direction inclined by the relative approach angle θ from the traveling direction of the vehicle C (Z-axis direction) with a width of the vehicle C in a direction perpendicular to the direction. Danger zone DZ is set. Also in this example, when the moving body is on the right side of the vehicle C, the risk level calculation unit 42 sets a danger zone inclined to the right by the relative approach angle θ, and when the moving body is on the left side of the vehicle C. Then, a danger zone tilted to the left by a relative approach angle θ is set. However, in this example, the upper limit of the danger zone is not set. In this example, since the distance D2 of the representative point is estimated, the intersection of the XZ ray of the representative point and the estimated value D2 of the representative point is the (X, Z) coordinate of the representative point. Therefore, whether or not the (X, Z) coordinates of the representative point are included in the danger zone DZ can be set as the danger level. That is, when the (X, Z) coordinates of the representative point are included in the danger zone DZ, it is determined to be dangerous, and when the (X, Z) coordinates of the representative point are not included in the danger zone DZ, it is dangerous. It is determined that it is not.
In FIG. 14, the intersection of the XZ ray (1) and the estimated distance value D2 is outside the danger zone DZ, so it is determined that it is not dangerous. Since the intersection between the XZ ray (2) and the estimated distance value D2 is in the danger zone DZ, it is determined to be dangerous.
FIG. 15 is a diagram for explaining a modification of this example. In this modification, as shown in FIG. 15, the danger zone DZ is divided into a plurality of levels and set in stages. In this modification, a danger zone DZ1 having a high danger level is set in an area including more front parts (including the front bumper) of the vehicle C, and a danger zone DZ2 having a medium danger level is set outside thereof. A danger zone DZ3 having a relatively low danger level is also set around. Then, similarly to the above, the danger level calculation unit 42 obtains a danger level corresponding to the danger zone including the intersection of the XZ ray and the estimated distance value D2. Such leveling of dangerous zones may also be performed in the first example and third and fourth examples described later.
Also in this example, in the XZ plane, the smaller the deviation between the direction of the relative approach angle θ and the direction of the position vector of the representative point (the higher the degree of coincidence), the higher the degree of risk. The risk level is calculated such that the closer the vanishing point and the representative point are, the higher the risk level is.
(Third example of risk calculation: calculation of risk based on probability distribution of distance)
FIG. 16 is a diagram for explaining calculation of the degree of risk based on the probability distribution of distance. The risk level calculation unit 42 calculates a boundary point BP (X BP , Z BP ) just on the boundary between the danger zone DZ and the safety zone SZ on the representative point XZ ray. The risk calculation unit 42 bisects the probability distribution of the distance estimated by the distance estimation unit 41 with the Z coordinate (Z BP ) of the boundary point BP as a boundary. Then, the integrated value S1 of the probability density function of the probability distribution on the risk zone DZ side with respect to the entire area of the probability distribution indicates the probability that the representative point is in the risk zone, and the probability on the safety zone SZ side with respect to the entire area of the probability distribution The integrated value S2 of the probability density function of the distribution indicates the probability that the representative point is in the safety zone.
The risk level calculation unit 42 sets the probability that the representative point is on the danger zone DZ side as the risk level of the representative point. When the distance distribution estimated by the distance estimation unit 41 is a normal distribution, an error function can be used for the integration. Further, the risk level calculation unit 42 may use the probability as the risk level as described above, or may calculate the risk level at a discretized level.
(Fourth example of risk calculation: calculation of risk based on estimated upper limit of distance and distance distribution)
FIG. 17 is a diagram for explaining the calculation of the risk based on the estimated upper limit of distance and the distribution of distance. In the fourth example, the distance estimation unit 41 calculates the upper limit of the distance and determines the probability distribution of the distance. The risk degree calculation unit 42 cuts the probability distribution of the distance determined by the distance estimation unit 41 by the upper limit estimated value D1 of the distance obtained by the distance estimation unit 41 to obtain a single cut normal distribution. Like the third example, the risk degree calculation unit 42 uses a distribution that is not more than the upper limit D1 of the distance, and the boundary point BP (X BP at the boundary between the danger zone DZ and the safety zone SZ on the XZ ray of the representative point). , Z BP ), and the single cut normal distribution is bisected using the Z coordinate (Z BP ) of the boundary point BP as a boundary.
The integral value of the probability density function in the distance range up to the upper limit value D1 of the XZ ray of the representative point, that is, the integral value of the single cut normal distribution is S1 + S2 ′, which is generally 100%. Must not. The integrated value of the probability density function in the distance range in the danger zone of the representative point XZ ray is S1 in FIG. In this example, the ratio S1 / (S1 + S2 ') of S1 with respect to S1 + S2' is the risk of this representative point. Also in this example, the risk level calculation unit 42 may use S1 / (S1 + S2 ') as the risk level as it is, or may calculate the risk level at a discrete level.
As described above, in any of the first to fourth examples of risk calculation, the risk is small in the XZ plane between the direction of the relative approach angle θ and the direction of the representative point position vector. In other words, the higher the coincidence, in other words, the higher the vanishing point and the representative point in the image, the higher the calculation. There are other examples of this. The collision risk degree calculation unit 40 determines, for example, the angle difference between the direction of the relative approach angle θ and the direction of the position vector of the representative point, that is, the distance between the vanishing point and the representative point in the image. May be calculated as
(Modification of risk calculation)
This modification can be added to the above first to fourth calculation examples. In this modification, determination is performed by adding TTC to the risk calculated in the first to fourth calculation examples. It can be said that an object with a small TTC is naturally more dangerous than an object with a large TTC. Therefore, in this modified example, the risk based on the magnitude of the TTC is added to the risk calculated in the first to fourth calculation examples so that the risk increases as the TTC decreases.
Specifically, when the risk level is obtained as a continuous value, the reciprocal of TTC is used as the TTC level, for example, by multiplying the risk level calculated in the above first to fourth calculation examples by the TTC level. The risk level can be corrected. If the risk level is obtained at discrete levels, for example, as shown in FIG. 18, the TTC value is divided into three levels, and the risk level is determined by combining the risk level and the TTC level. Can be corrected.
As described above, the distance estimation unit 41 and / or the risk level calculation unit 42 estimate distance and / or calculate the risk level using one point as a representative point from a plurality of feature points grouped as a moving object. However, the distance estimation unit 41 and / or the risk level calculation unit 42 specifies a plurality of representative points for one moving body, performs distance estimation and / or risk level calculation for each representative point, It is also possible to take the average of a plurality of risk levels calculated at the representative point of the number of points, and to determine the risk level that the moving body will collide with the host vehicle. In addition, the representative point is a point determined based on a plurality of feature points, such as a center point or a center of gravity point of the plurality of feature points, without being one of the plurality of feature points grouped as the moving body. It may be.
The notification unit 50 notifies the driver according to the collision risk calculated by the collision risk calculation unit 40. The mobile object recognition system 100 includes a display such as a head-up display. An image captured by the camera 10 is displayed on the display in real time. A rectangle surrounding the feature point group grouped by the moving object detection unit 20 is superimposed on this image. At this time, the notification unit 50 changes the color and thickness of the rectangle according to the degree of risk. Further, the notification unit 50 may notify the driver of the danger of collision by sound. In this case, the driver may be alerted by increasing the sound or increasing the frequency as the degree of danger increases.
An automatic braking system may be provided in addition to or in place of the notification unit 50. The automatic braking system receives information on the degree of risk calculated by the degree-of-risk calculation unit 40 and performs a braking operation on the vehicle. This automatic braking system enables automatic braking of the vehicle in dangerous situations. Further, a storage device may be provided in addition to or in place of the notification unit 50. The storage device stores risk information calculated by the risk calculation unit 40 together with its position information and time information. By referring to the record of the degree of danger, it is possible to grasp the dangerous area and the dangerous time zone, and to grasp the situation of the driver's safe driving.
As described above, the mobile object recognition system 100 according to the embodiment of the present invention acquires a plurality of continuous monocular images with a camera installed in a vehicle, and groups feature points in the images by vanishing points. Since the moving object is detected from the image and the relative approach angle is estimated from the image, the risk of the moving object colliding with the vehicle can be calculated using the relative approach angle. Since this risk level is a physically meaningful index, the driver can intuitively know the dangerous situation by reporting according to the risk level, or according to the dangerous situation. Control can be performed. In addition, since the mobile object recognition system 100 according to the embodiment of the present invention calculates the degree of danger so that the deviation between the direction of the relative approach angle and the direction of the detected position vector of the mobile object is small, The risk of collision can be calculated suitably.
Further, since a relatively inexpensive monocular camera is used without calculating a stereo camera or a distance sensor for calculating the risk of collision, the cost can be reduced. In addition to the above configuration, the mobile object recognition system 100 according to the embodiment of the present invention may use a distance sensor in combination. In this case, the mobile object recognition system 100 uses the information on the trajectory of the mobile object obtained by the distance sensor and the information such as the relative approach angle, the collision time, and the estimated distance to the mobile object obtained by the above configuration. By comparing, the certainty of detection can be enhanced. When the distance sensor is used in combination, the distance to the moving body obtained by the distance sensor can also be used to improve the calculation accuracy of the risk of collision of the moving body.
Further, in detecting a moving object, since a connected optical flow is created to remove an outlier, the detection accuracy of the moving object can be improved.
INDUSTRIAL APPLICABILITY The present invention has an effect that a risk that a moving body collides with the vehicle can be obtained as useful information for safe driving of a vehicle driver, and movement that recognizes the moving body using a single viewpoint image This is useful as a body recognition system.
DESCRIPTION OF SYMBOLS 100 Mobile body recognition system 10 Camera 20 Mobile body detection part 21 Connection optical flow calculation part 22 Rotation movement amount and vanishing point estimation part 23 Background point removal part 24 Grouping part 30 Relative approach angle estimation part 40 Collision risk degree calculation part 41 Distance Estimator 42 Risk level calculator 50 Notifier
A camera that is installed in a vehicle and shoots a plurality of continuous single viewpoint images;
Using a plurality of images taken by the camera, a moving body detection unit that detects a moving body in the image,
A relative approach angle estimation unit that estimates a relative approach angle of the mobile body detected by the mobile body detection unit to the camera;
A collision risk calculation unit that calculates a risk that the moving body collides with the vehicle based on a relationship between the relative approach angle and a moving body direction from the camera toward the moving body;
A mobile object recognition system comprising:
The mobile object recognition system according to claim 1, wherein the collision risk degree calculation unit increases the risk degree as the deviation between the relative approach angle and the moving body direction is smaller.
The movement according to claim 1 or 2, wherein the collision risk calculation unit sets a danger zone based on the relative approach angle, and calculates the risk based on a probability that the moving body exists in the danger zone. Body recognition system.
The danger zone is set on the XZ plane when the camera is the origin, the optical axis direction of the camera is the Z direction, the vertical direction is the Y direction, and the horizontal direction is the X direction. The mobile object recognition system according to claim 3, wherein the mobile object recognition system is a region that extends in a direction of the relative approach angle with a width.
The collision risk calculation unit includes a distance estimation unit that estimates an upper limit value of the distance in the Z direction from the camera to the moving body, and the moving body on the image that is separated from the camera and the camera by a focal length. The risk of calculating, as the degree of risk, the ratio of the line segment that enters the danger zone among the line segments from the camera to the upper limit value of the XZ ray that is an orthogonal projection of the straight line passing through the XZ plane. The mobile object recognition system according to claim 4, further comprising a degree calculation unit.
The moving object detection unit extracts a plurality of feature points from the image, calculates an optical flow of the extracted feature points, and extends the optical flow among the plurality of feature points. A grouping unit that groups a plurality of feature points that converge to one vanishing point as a plurality of feature points on the moving body,
The distance estimation unit selects a lowest feature point having the lowest height from a plurality of feature points on the grouped moving body, and a vehicle connecting a straight line connecting the lowest feature point and the optical center to the vehicle. The moving body according to claim 5, wherein an intersection with the traveling ground is a ground point, and a Z coordinate of the ground point is estimated as an upper limit value of a distance in the Z direction from the camera to the moving body. Recognition system.
The collision risk calculation unit passes through a distance estimation unit that estimates a distance in the Z direction from the camera to the moving body, and the moving body on the image that is separated from the camera by a focal length. When the point having the estimated distance from the camera on the XZ ray which is a straight line projection of a straight line onto the XZ plane is in the danger zone, the risk is higher than when the point is not in the danger zone. The mobile object recognition system according to claim 4, further comprising a risk degree calculation unit that calculates a degree.
The distance estimating unit obtains a collision time until the moving body collides with the vehicle based on the vanishing point, and the Z direction from the camera to the moving body based on the collision time and the speed of the vehicle. The mobile object recognition system according to claim 7, wherein the distance is estimated.
The collision risk calculation unit includes a distance estimation unit that determines a probability distribution of the distance in the Z direction from the camera to the moving body, and the moving body on the image that is separated from the camera and the camera by a focal length. A risk calculation for calculating the risk based on an integral value of a probability density function of the probability distribution in a distance range in which an XZ ray that is a straight line projecting a straight line passing through the XZ plane is in the risk zone The mobile body recognition system according to claim 4, further comprising: a unit.
The distance estimation unit obtains a collision time until the moving object collides with the vehicle based on the vanishing point, and determines a probability distribution of the distance according to a predetermined normal distribution based on the collision time. The moving body recognition system according to claim 9, wherein
The collision risk calculation unit estimates an upper limit value of the distance in the Z direction from the camera to the moving body, and determines a probability distribution of the distance in the Z direction from the camera to the moving body. A line passing through the camera and the moving body on the image separated from the camera by a focal length with respect to an integral value of a probability density function of the probability distribution in a distance range up to the upper limit value in the XZ plane A risk calculation unit that calculates a risk based on a ratio of an integral value of a probability density function of the probability distribution in a distance range in which the XZ ray that is orthogonally projected to the risk zone. The moving body recognition system according to claim 4, wherein
The distance estimation unit selects a lowest feature point having the lowest height from a plurality of feature points on the grouped moving body, and a vehicle connecting a straight line connecting the lowest feature point and the optical center to the vehicle. The intersection point with the traveling ground is set as a ground point, the Z coordinate of the ground point is estimated as an upper limit value of the distance in the Z direction from the camera to the moving body, and the moving body is based on the vanishing point. The mobile object recognition system according to claim 11, wherein a collision time until the vehicle collides is obtained, and a probability distribution of the distance is determined according to a predetermined normal distribution based on the collision time.
The mobile object recognition system according to any one of claims 3 to 12, wherein the collision risk degree calculation unit divides the danger zone into a plurality of levels and sets them in stages.
Using a plurality of images photographed by the camera, extracting a plurality of feature points from the image, generating an optical flow of the extracted feature points, and extending the plurality of feature points A moving body detection unit that detects a plurality of grouped feature points as a moving body by grouping a plurality of feature points where the optical flow converges to one vanishing point;
A collision risk calculation unit for calculating a risk of collision of the mobile body with the vehicle;
The collision risk degree calculation unit is configured such that the danger level of the moving object whose distance from the vanishing point in the image is short is the risk degree of the moving object whose distance from the vanishing point in the image is long. The mobile object recognition system characterized by calculating the said risk level so that it may become higher.
The collision risk degree calculation unit uses the feature point of any of the plurality of feature points on the moving body or a point obtained from the plurality of feature points on the moving body as a representative point. 15. The moving object recognition system according to claim 6, 8, 10, 12, or 14, wherein the risk of collision with the vehicle is calculated.
The mobile body according to any one of claims 1 to 15, further comprising an informing unit that informs the driver of the vehicle of the danger according to the degree of risk calculated by the collision risk degree calculating unit. Recognition system.
The mobile object according to any one of claims 1 to 16, wherein the collision risk calculation unit corrects the risk based on a collision time until the mobile object collides with the vehicle. Recognition system.
A moving body detection unit that detects a moving body in an image using a plurality of images captured by a camera that is installed in a vehicle and captures a plurality of continuous single viewpoint images;
A relative approach angle estimator for estimating a relative approach angle of the mobile body detected by the mobile body detector with respect to the camera; and the relative approach angle; and a mobile body direction from the camera toward the mobile body. Based on the relationship, a collision risk calculation unit that calculates a risk that the moving body will collide with the vehicle,
A moving body recognition program that makes it work.
A plurality of feature points are extracted from the images using a plurality of images that are installed in a vehicle and photographed by a camera that captures a plurality of continuous single viewpoint images, and an optical flow of the extracted feature points is obtained. Generating and grouping a plurality of feature points where the extended optical flow converges to one vanishing point among the plurality of feature points, and detecting the plurality of grouped feature points as a moving object A body detection unit, and a collision risk calculation unit that calculates a risk that the moving body collides with the vehicle, wherein the risk of the moving body that is close to the vanishing point in the image is It is made to function as a collision risk calculation unit for calculating the risk so that the distance from the vanishing point in the image is higher than the risk of the moving object that is far. Moving object recognition program.
A shooting step of shooting a plurality of continuous single viewpoint images with a camera installed in the vehicle;
A moving object detection step for detecting a moving object in the image using a plurality of images taken by the camera;
A relative approach angle estimation step of estimating a relative approach angle of the mobile body detected in the mobile body detection step with respect to the camera;
A collision risk calculating step for calculating a risk that the moving body will collide with the vehicle based on a relationship between the relative approach angle and a moving body direction from the camera toward the moving body;
A moving body recognition method comprising:
Using a plurality of images photographed by the camera, extracting a plurality of feature points from the image, generating an optical flow of the extracted feature points, and extending the plurality of feature points A moving body detecting step of detecting the plurality of grouped feature points as a moving body by grouping a plurality of feature points where the optical flow converges to one vanishing point;
A collision risk calculating step of calculating a risk of collision of the moving body with the vehicle;
In the collision risk calculation step, the risk of the moving object having a short distance from the vanishing point in the image is the risk of the moving object having a long distance from the vanishing point in the image. The moving body recognition method, wherein the risk level is calculated so as to be higher than that.
JP2012169709A 2012-07-31 2012-07-31 Mobile object recognition system, mobile object recognition program, and mobile object recognition method Active JP5944781B2 (en)
JP2012169709A JP5944781B2 (en) 2012-07-31 2012-07-31 Mobile object recognition system, mobile object recognition program, and mobile object recognition method
CN201210407065.9A CN103578115B (en) 2012-07-31 2012-10-23 Moving body identifying system and moving body recognition methods
EP15175825.7A EP2960886A1 (en) 2012-07-31 2012-11-29 Moving object recognition systems, moving object recognition programs, and moving object recognition methods
EP12194786.5A EP2693416A3 (en) 2012-07-31 2012-11-29 Moving object recognition systems, moving object recognition programs, and moving object recognition methods
US13/689,196 US9824586B2 (en) 2012-07-31 2012-11-29 Moving object recognition systems, moving object recognition programs, and moving object recognition methods
JP2014029604A true JP2014029604A (en) 2014-02-13
JP5944781B2 JP5944781B2 (en) 2016-07-05
ID=47355839
JP2012169709A Active JP5944781B2 (en) 2012-07-31 2012-07-31 Mobile object recognition system, mobile object recognition program, and mobile object recognition method
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JP (1) JP5944781B2 (en)
CN (1) CN103578115B (en)
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