Source: https://insight.rpxcorp.com/pat/US7209832B2
Timestamp: 2020-02-18 22:48:35
Document Index: 801213664

Matched Legal Cases: ['art 104', 'art 141', 'art 141', 'art 142', 'art 141', 'art 141', 'art 142', 'art 142', 'art 141', 'art 142', 'art 142', 'art 142', 'art 141', 'art 142', 'art 142', 'art 142', 'art 141', 'art 141', 'art 141', 'art 141', 'art 142', 'art 142', 'art 142', 'art 142', 'art 142', 'art 141', 'art 142', 'art 142', 'art 142', 'art 142', 'art 142', 'art 142', 'art 142', 'art 131', 'art 131', 'art 131', 'art 103', 'art 106', 'art 131', 'art 103', 'art 131', 'art 131', 'art 131', 'art 106', 'art 131', 'art 131', 'art 131', 'art 131', 'art 131', 'art 131', 'art 131', 'art 131', 'art 131', 'art 103', 'art 131', 'art 131', 'art 131', 'art 131', 'art 131', 'art 103', 'arts 131', 'art 103', 'art 131', 'art 131', 'art 131', 'art 106', 'art 131']

Patent US 7,209,832 B2
a lane recognition part deriving a lane marking mathematical model equation by approximating sets of candidate points extracted by said candidate point extraction part by a mathematical model equation,wherein said candidate point extraction part comprises;
a kernel size setting part setting a kernel size Δ
h in accordance with a forward distance from said vehicle;
a one-dimensional image filtering part outputting, as a filtering result, the smaller one of the values that are obtained by two equations {g(h)−
g(h−
h)} and {g(h)−
g(h+Δ
h)} using the gray value g(h) of a pixel of interest and the gray values g(h−
h), g(h+Δ
h) of pixels forwardly and rearwardly apart said kernel size Δ
h from said pixel of interest in a scanning direction, respectively; and
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6. The lane recognition image processing apparatus as set forth in claim 1, whereinsaid at least one lane marking comprises a pair of right and left lane markings, and said apparatus further comprises a vanishing point learning part obtaining a vanishing point learning position based on said right and left lane markings;
The candidate point extraction part 104 includes a one-dimensional image filtering part 141 with a kernel size setting part 141a, a binarization part 142 for binarizing the filtering results E of the one-dimensional image filtering part 141 by means of thresholds.
The kernel size setting part 141a sets a kernel size Δh in accordance with a forward distance from the vehicle 2.
The binarization part 142 includes a multi-threshold setting part 142a for setting a threshold (described later) for each of search scanning lines of the one-dimensional image filtering part 141, an S/N ratio calculation part 142b for calculating an S/N ratio Rs of each filtering result E, and a threshold lower limit setting part 142c for setting a lower limit for the thresholds based on the S/N ratio Rs.
The S/N ratio calculation part 142b counts the number of filter pass ranges having their range widths less than a specified value as the number of noise ranges Mn in the filtering result E, also counts the number of filter pass ranges having their range widths more than or equal to the specified value as the number of signal ranges Ms in the filtering result E, and calculates the S/N ratio Rs based on the number of noise ranges Mn and the number of signal ranges Ms thus obtained.
The basic hardware configuration of the lane recognition image processing apparatus shown in FIG. 1 is common to that of the conventional apparatus, but includes, as its concrete or detailed processing contents, the kernel size setting part 141a, the multi-threshold setting part 142a, the S/N ratio calculation part 142b, and the threshold lower limit setting part 142c.
Now, a concrete processing operation of the lane recognition image processing apparatus according to the first embodiment of the present invention as illustrated in FIGS. 1 and 2 will be described while referring to FIG. 3 through FIG. 6.
Moreover, the kernel size setting part 141a sets the kernel size Δh in accordance with the forward distance from the vehicle.
FIG. 10 is an explanatory view that shows the processing of the kernel size setting part 141a, illustrating the state of the T-H filtering kernel size Δh being variably set in accordance with the forward distance.
Here, the distance between the point Po of interest and the reference point Pa and the distance between the point Po of interest and the reference point Pb are respectively called the kernel size Δh, which is set in accordance with the forward distance by the kernel size setting part 141a, as shown in FIG. 10. The width of a filter pass range (to be described later) is set by the kernel size Δh.
Specifically, the kernel size setting part 141a individually sets the kernel size Δh for each search line Vn (i.e., in accordance with the forward distance), as shown in FIG. 10.
Here, reference will be made to a process of setting the threshold Th by means of the multi-threshold setting part 142a in the binarization part 142 while referring to FIG. 11 and FIG. 12.
The multi-threshold setting part 142a in the binarization part 142 individually sets a threshold Th for each search line Vn, similar to the setting of the kernel size Δh (see FIG. 10).
That is, in the multi-threshold setting part 142a, by setting a threshold Th for each search line (search scanning line) Vn of the one-dimensional image filtering part 141, and by setting a proper distant threshold Thb (<Tha) with respect to a distant image whose contrast is lower than that of a near image, as shown in FIG. 12, the distant lane marking recognition performance can be improved.
Now, reference will be made to an arithmetic process of calculating the S/N ratio Rs by means of the S/N ratio calculation part 142b while referring to FIG. 13.
The S/N ratio calculation part 142b detects noise ranges 20 together with signal ranges, as shown in FIG. 13, calculates an S/N ratio Rs from the number of the signal ranges and the number of the noise ranges, and utilizes the S/N ratio Rs thus obtained as a setting condition for the threshold Th.
In FIG. 13, first of all, the S/N ratio calculation part 142b extracts filter pass ranges (see shaded portions) by utilizing the threshold Th with respect to the filtering result E.
Rs=Ms/(Ms+Mn)×100[%] (2)
Next, reference will be made to a process of setting a lower limit of the threshold Th by means of the threshold lower limit setting part 142c.
The threshold lower limit setting part 142c sets the lower limit of the threshold Th based on the S/N ratio Rs calculated by the S/N ratio calculation part 142b. Specifically, the threshold Th is controlled so as to keep the S/N ratio Rs to be constant.
Thus, by setting the lower limit of the threshold Th based on the S/N ratio Rs of the image in the threshold lower limit setting part 142c so as to reduce false detection that would otherwise result from an excessive decrease or lowering of the threshold Th, it is possible to greatly reduce the false detection due to such an excessive lowering of the threshold Th with respect to images containing a lot of noise.
Although in the above-mentioned first embodiment, only the model equation reference part is used in the setting of a window, a candidate point reference part 131b and a vanishing point reference part 131c can be added to or incorporated in the reference position setting part 131 in a window setting part 103A, and a vanishing point learning part 106 can also be provided for optimally setting the window W for each search line Vn, as shown in FIG. 15.
In FIG. 15, a major difference from the above-mentioned first embodiment (FIG. 1) is that the reference position setting part 131 in the window setting part 103A incorporates therein not only the model equation reference part 131a but also the candidate point reference part 131b and the vanishing point reference part 131c, and at the same time, provision is made for the vanishing point learning part 106 in conjunction with the vanishing point reference part 131c.
The reference position setting part 131 includes the model equation reference part 131a, the candidate point reference part 131b, and the vanishing point reference part 131c, so that either one of the model equation reference part 131a, the candidate point reference part 131b and the vanishing point reference part 131c can be selected to set the reference positions of windows W to search for the lane markings 3, 4.
The model equation reference part 131a in the window setting part 103A serves to set the reference positions of the windows W on each search line Vn from the above-mentioned lane marking mathematical model equations.
FIG. 16 is an explanatory view that shows the processing of the candidate point reference part 131b, wherein attention is expediently focused on the left lane marking 3 alone so as to set a reference position with respect to a left window W1.
In FIG. 16, in cases where there exist two or more candidate points Pq, Pr, the candidate point reference part 131b in the reference position setting part 131 sets the window W1 on the following search line Vn based on a straight line Lqr connecting between the two adjacent candidate points Pq, Pr. That is, the window W1 is set based on an intersection Px between the straight line Lqr and the search line Vn.
FIG. 17 is an explanatory view that shows the processing of the vanishing point reference part 131c, wherein similar to the case of FIG. 16, attention is focused on the left lane marking 3 alone so as to set a reference position with respect to the left window W1.
In FIG. 17, in cases where there exists a single candidate point Pq alone, the vanishing point reference part 131c in the window setting part 103A sets the window W1 on the following search line Vn based on a straight line Lqz connecting between the near candidate point Pq and a vanishing point Pz. That is, the window W1 is set based on an intersection Py between the straight line Lqz and the search line Vn.
In FIG. 19, steps S20 through S22 represent a determination process for selecting the reference parts 131a through 131c, respectively, and steps S23 through S26 represent a process for setting a reference position of each window W based on the results of determinations in the respective steps S20 through S22.
The window setting part 103A first determines whether there exists any lane marking mathematical model equation (step S20), and when determined that a lane marking mathematical model equation exists (i.e., Yes), it then selects the model equation reference part 131a. That is, similar to the above, the position of the line Lqr on a search line Vn is calculated from the lane marking mathematical model equation, and it is decided as the reference position of the window W (step S23).
When determined in step S21 that two or more candidate points have been extracted (i.e., Yes), the candidate point reference part 131b is selected, so that it decides an intersection Px between the straight line Lqr connecting the candidate point Pq and the candidate point Pr and the following search line Vn as a reference position, as shown in FIG. 16 (step S24).
When determined in step S22 that a single candidate point alone has been extracted (i.e., Yes), the vanishing point reference part 131c is selected, so that it decides as a reference position an intersection Py between a straight line Lqz connecting the near candidate point Pq and the vanishing point Pz and the following search line Vn, as shown in FIG. 17 (step S25).
Here, note that the vanishing point learning part 106 obtains the learning coordinates of the vanishing point Pz from the approximate straight lines Lz1, Lz2 of the right and left lane markings 3, 4, as shown in FIG. 18, and inputs them to the vanishing point reference part 131c, thus contributing to the reference position setting process in step S25.
Yamamoto, Takayuki, Ishikura, Hisashi, Fujie, Kenichi, Fujii, Yoshiyuki, Sugiyama, Akinobu
US 20060015252A1
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