Source: https://insight.rpxcorp.com/pat/US6590521B1
Timestamp: 2019-11-12 04:21:32
Document Index: 340686391

Matched Legal Cases: ['art 21', 'art 21', 'art 21', 'art 5', 'art 7', 'art 9', 'art 7', 'art 9', 'art 7', 'art 15', 'arts 7', 'art 23', 'art 23', 'art 31', 'art 34', 'art 33', 'art 32', 'art 25', 'art 23', 'art 21', 'art 25', 'art 21', 'art 23', 'art 25', 'art 25', 'art 25', 'art 25', 'art 5', 'art 7', 'art 9', 'art 15', 'art 31', 'art 33', 'art 34', 'art 35', 'art 25']

Patent US 6,590,521 B1
a radar for determining a position of an object;
a controller programmed to position a processing area within the image captured by the image sensor with respect to the position of the object determined by the radar, to recognize the outline of the object based on edges extracted from the processing area, to recognize lane lines defining a lane in which the vehicle mounting the system is running, and to determine a relative position of the object to the lane lines based on the outline of the object and the recognized lane lines, wherein the size of the processing area is predetermined to adapt to a possible width and height of the object to be recognized.
An object recognition system including a radar, an image sensor and a controller is provided. The radar determines the position of an object, and the image sensor captures an image of the object. The controller sets a processing area within the image captured by the image sensor based on the position of the object determined by the radar and a predetermined size for the object to be recognized. The controller extracts horizontal and vertical edges from the processing area, and preferably judges whether each of the extracted edges belongs to the object based on characteristics of the object to be recognized. The controller then recognizes the outline of the object based on the edges judged to belong to the object. The object can be recognized by determining upper, lower, left and right ends of the object. On the other hand, the controller recognizes lane lines defining the lane in which the vehicle mounting the system of the invention is running. Thus, the relative position of the object to the lane lines is determined by comparing the intersections between a horizontal line corresponding to the lower end of the object and the lane lines with the left and right ends of the object.
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2. The system of claim 1, wherein the outline of the object is recognized by determining upper, lower, left, and right ends of the object.
6. The system of claim 1, said controller is further programmed to detect a road area by examining a change in intensity for pixels within the image;
and wherein the lane lines are determined from an outline of the detected road area.
7. The system of claim 1, wherein the size of the processing area is predetermined allowing for a margin.
determining a position of the object;
positioning a processing area within the image with respect to the determined position of the object;
extracting edges from the processing area;
recognizing an outline of the object based on the extracted edges;
recognizing lane lines defining a lane in which the vehicle implementing the method is running; and
determining a relative position of the object to the lane lines based on the recognized outline of the object and the recognized lane lines, wherein the size of the processing area is predetermined to adapt to a possible width and height of the object to be recognized.
9. The method of claim 8, further comprising a step of determining upper, lower, left, and right ends of the object;
and wherein the outline of the object is recognized by determined upper, lower, left, and right ends of the object.
10. The method of claim 9, further comprising a step of comparing the intersections between a horizontal line corresponding to the lower end of the object and the lane lines with the left and right ends of the object;
and wherein the relative position is determined based on the result of the comparison.
11. The method of claim 10, wherein the edges comprises horizontal edges and vertical edges.
13. The method of claim 12, further comprising a step of judging whether each of the extracted horizontal and vertical edges belongs to the object based on characteristics of the object to be recognized;
and wherein the step of recognizing the outline of the object is performed on the horizontal and vertical edges judged to belong to the object.
14. The method of claim 8, further comprising a step of detecting a road area by examining a change in intensity for pixels within the image;
15. An object recognition system comprising:
means for positioning a processing area within the image with respect to the position determined by the radar;
means for extracting edges from the processing area;
means for recognizing an outline of the object based on the extracted edges;
means for recognizing lane lines defining a lane in which the vehicle mounting the system is running; and
means for determining a relative position of the object to the lane lines based on the recognized outline of the object and the recognized lane lines, wherein the size of the processing area is predetermined to adapt to a possible width and height of the object to be recognized.
16. The system of claim 15, wherein the outline of the object is recognized by determining upper, lower, left, and right ends of the object.
20. The system of claim 15, further comprising means for detecting a road area by examining a change in intensity for pixels within the image;
and wherein the lane lines are determined from the outline of the detected road area.
The present invention relates to an object recognition system which is mounted on a vehicle and determines the relative position of an object ahead of the vehicle, and more particularly to an object recognition system determines the relative position of a vehicle ahead to the vehicle mounting the system.
According to one aspect of the invention, an object recognition system including a radar, an image sensor, and a controller is provided. The radar determines the position of an object, and the image sensor captures an image of the object. The controller sets a processing area within the image captured by the image sensor based on the position of the object determined by the radar and a predetermined size for the object to be recognized. It is preferable that the predetermined size is set to surround the object to be recognized.
<FGREF>FIG. 1</FGREF> is a block diagram illustrating the overall structure, and functional blocks of the controller of one embodiment of the present invention.
<FGREF>FIG. 2</FGREF> is a block diagram illustrating in detail the object recognition part in accordance with one embodiment of the present invention.
<FGREF>FIG. 3</FGREF> is a diagram showing the processing area in accordance with one embodiment of the present invention.
<FGREF>FIGS. 4</FGREF>a and 4b are a diagram showing the way for setting a processing area in accordance with one embodiment of the present invention.
<FGREF>FIG. 5</FGREF> is a diagram showing another processing area with allowance for pitching in accordance with one embodiment of the present invention.
<FGREF>FIG. 6</FGREF> is a flowchart of the method for extracting edges.
<FGREF>FIG. 8</FGREF> is a histogram showing intensity values of the captured image.
<FGREF>FIGS. 9</FGREF>a through 9e are a diagram illustrating the template and method of determining labels used in accordance with one embodiment of the present invention.
<FGREF>FIGS. 10</FGREF>a through 10c are a diagram illustrating labeling scheme in accordance with one embodiment of the present invention.
<FGREF>FIG. 11</FGREF> is a diagram showing the filter for vertical edges.
<FGREF>FIGS. 12</FGREF>a through 12c are a diagram showing the scheme for determining horizontal edges in accordance with one embodiment of the present invention.
<FGREF>FIG. 13</FGREF> is a flowchart of a method for recognizing the outline of an object in accordance with one embodiment of the invention.
<FGREF>FIGS. 14</FGREF>a through 14f are a diagram showing the scheme for recognizing the outline of the object in accordance with one embodiment of the present invention.
<FGREF>FIG. 15</FGREF> is a block diagram illustrating in detail the lane line detection part in accordance with one embodiment of the present invention.
<FGREF>FIGS. 16</FGREF>a through 16d are a diagram showing the scheme for detecting the lane lines.
<FGREF>FIG. 17</FGREF> is a block diagram illustrating the overall structure, and functional blocks of the controller of another embodiment of the present invention.
<FGREF>FIGS. 18</FGREF>a through 18d are a diagram showing the scheme for determining the relative position of the object to the lane lines of the vehicle mounting the system of the invention in accordance with one embodiment of the invention.
The embodiments of the present invention will be described below with reference to the attached drawings. <FGREF>FIG. 1</FGREF> is an overall block diagram of an object recognition system in accordance with one embodiment of the present invention. Other than the image sensor 1 and the object position sensor 3, all the blocks in <FGREF>FIG. 1</FGREF> may be incorporated in a controller which comprises a single chip or multiple chip semiconductor integrated circuit. Thus, <FGREF>FIG. 1</FGREF> shows functional blocks of the controller. Respective functions of the blocks are performed by executing respective programs stored in the ROM of the controller.
The image sensor 1 shown in <FGREF>FIG. 1</FGREF> captures a view ahead of the vehicle mounting the system of the invention. The image sensor 1 is typically two-dimensional CCDs, and can be two-dimensional photo-sensor arrays. When usage in the night is considered, an image sensor using infrared light is advisable. In this case, it is preferable to install infrared-transparent filters in front of a lens, and to design the system such that the object is illuminated at a predetermined period from an infrared light source. The image sensor senses the infrared light reflected from the object. The image captured by the image sensor 1 is converted into digital data by an analog-to-digital converter (not shown), and is stored in an image memory 2.
An object recognition part 21 shown in <FGREF>FIG. 1</FGREF> sets a processing area within the image captured by the image sensor 1, and recognizes the vehicle ahead using edges extracted from the processing area. The process of recognizing the object is repeatedly executed at predetermined time intervals (for example, 100 milliseconds). Described below in detail is the method for recognizing the object implemented by the object recognition part 21, with reference to <FGREF>FIG. 2</FGREF> showing the object recognition part 21 in detail.
A processing area setting part 5 shown in <FGREF>FIG. 2</FGREF> sets processing area within the image captured and stored in the image memory 2 based on the position of the object stored in the object position memory 4 and a predetermined size for the object to be recognized. The predetermined size for the object to be recognized is set beforehand to surround the object to be recognized.
The process of setting the processing area is described below by referring to <FGREF>FIGS. 3</FGREF>, 4a, and 4b. <FGREF>FIG. 3</FGREF> shows an example of the captured image in which the vehicle ahead 40 running forward is included. As shown in <FGREF>FIG. 3</FGREF>, an x-axis and a y-axis are fixed in the image, and a processing area 30 is defined by the coordinates (Xa1, Ya1) and (Xa2, Ya2).
<FGREF>FIGS. 4</FGREF>a and 4b show the way for setting the processing area 30. FIG. 4(A) shows the way for determining the x coordinates, that is, Xa1 and Xa2, and FIG. 4(B) shows the way for determining the y coordinates, that is, Ya1 and Ya2.
In <FGREF>FIGS. 4</FGREF>a and 4b, the image sensor 1 is mounted on the vehicle mounting the system of the invention. The image sensor 1 captures the vehicle ahead 40 that is traveling in front of the vehicle mounting the system of the invention. Reference character f denotes the focal length of the lens 45 mounted on the image sensor 1, which is specified depending on the characteristic of the lens. Reference characters W and H denote predetermined width and height for the object to be recognized, that is, the vehicle ahead in the present embodiment, respectively. The width and height are preset to surround the object to be recognized. For example, for the vehicle ahead, W may be set to 2 m, and H may be set to 2.5 m. Reference characters D and θ denote the distance to the vehicle ahead and the relative direction of the vehicle ahead stored in the object position memory 4 respectively. Reference character h denotes the height from the road to the center of the lens 45, which is predefined depending on the position of the image sensor 1 in the vehicle mounting the system of the invention.
<F>Xa1=;(D×tan θ−(W/2))×(f/D) (1)</F>
<F>Xa2=;(D×tan θ+;(W/2))×(f/D) (2)</F>
<F>Ya1=;(H−h)×(f/D) (3)</F>
<F>Ya2=;−(h×(f/D)) (4)</F>
<F>Ya1=;(H−h)×(f/D)+;α (5)</F>
<F>Ya2=;(h×(f/D)+;α) (6)</F>
Thus, the processing area 30 is defined within the captured image by the coordinates (Xa1, Ya1) and (Xa2, Ya2) as shown in <FGREF>FIG. 3</FGREF>, or is defined as shown in <FGREF>FIG. 5</FGREF> with the pitching taken into account.
A horizontal edge extraction part 7 and a vertical edge extraction part 9 shown in <FGREF>FIG. 2</FGREF> extract horizontal edges and vertical edges respectively from the processing area 30. Since both horizontal and vertical edges are extracted in the same way, only the process of extracting horizontal edges is described below. The extracted edges show a portion in which the variation of intensity is large in the image. <FGREF>FIG. 6</FGREF> is a flowchart of extracting edges, which is carried out by the horizontal edge extraction part 7.
A computation shown in the equation (7) is executed for the intensity value of each pixel within the processing area 30 while the processing area 30 is scanned by the horizontal edge filter. <CWU><MATH-US><MATHEMATICA></MATHEMATICA><MATHML><math><mtable><mtr><mtd><mrow><mrow><mi>P</mi><mo>⁡;</mo><mrow><mo>(</mo><mrow><mi>x</mi><mo>,</mo><mi>y</mi></mrow><mo>)</mo></mrow></mrow><mo>=</mo><mrow><munderover><mo>∑;</mo><mrow><mi>i</mi><mo>=</mo><mrow><mo>-</mo><mn>1</mn></mrow></mrow><mn>1</mn></munderover><mo>⁢;</mo><mstyle><mtext> </mtext></mstyle><mo>⁢;</mo><mrow><munderover><mo>∑;</mo><mrow><mi>j</mi><mo>=</mo><mrow><mo>-</mo><mn>1</mn></mrow></mrow><mn>1</mn></munderover><mo>⁢;</mo><mstyle><mtext> </mtext></mstyle><mo>⁢;</mo><mrow><mo>{</mo><mrow><mrow><mi>F</mi><mo>⁡;</mo><mrow><mo>(</mo><mrow><mi>i</mi><mo>,</mo><mi>j</mi></mrow><mo>)</mo></mrow></mrow><mo>×</mo><mrow><mi>G</mi><mo>⁡;</mo><mrow><mo>(</mo><mrow><mrow><mi>x</mi><mo>+</mo><mi>i</mi></mrow><mo>,</mo><mrow><mi>y</mi><mo>+</mo><mi>j</mi></mrow></mrow><mo>)</mo></mrow></mrow></mrow><mo>}</mo></mrow></mrow></mrow></mrow></mtd><mtd><mrow><mo>(</mo><mn>7</mn><mo>)</mo></mrow></mtd></mtr></mtable></math></MATHML><EMI></EMI></MATH-US></CWU>
In another embodiment, instead of the equation (7), the filtering process is carried out by differentiation. In this case, the difference in intensity between vertically adjacent pixels is calculated as shown in equation (8), where n is an integer, for example, may be set to 1 (n=;1).
<F>P(x, y)=;G(x, y−n)−G(x, y+;n) (8)</F>
Then, an intensity histogram is created based on the intensity value P of each pixel (step 63). The intensity value used in this embodiment is represented as digital data having 256 gradations (ranging from pure black “0” to pure white “255”) <FGREF>FIG. 8</FGREF> shows an example of the intensity histogram. The horizontal axis indicates the intensity values obtained in the filtering process while the vertical axis indicates the number of pixels corresponding to each of the intensity values.
With reference to FIGS. (9A) through 9(E), the labeling process is described below. <FGREF>FIG. 9</FGREF> shows a template for the labeling process. T1 through T3 in FIG. 9(A) indicate positions in the template. V1 through V3 in FIG. 9(B) indicate the values (1 or 0) of pixels corresponding to the positions T1 through T3 respectively when the template is positioned such that T2 assumes the place of an edge point to be processed. L1 through L3 in FIG. 9(C) indicate labels assigned to pixels corresponding to the positions T1 through T3 respectively.
The vertical edge extraction part 9 extracts vertical edges from the processing area 30 in the same way as the horizontal edge extraction part 7 except that a vertical edge filter shown in <FGREF>FIG. 11</FGREF> is used in step 61 (FIG. 6).
Referring to <FGREF>FIG. 2</FGREF> again, an object outline recognition part 15 recognizes the outline of the object based on edges judged to belong to the object and stored in the horizontal edge memory 12 and the vertical edge memory 14. According to the present embodiment, since the object to be recognized is the vehicle ahead, the object is displayed as a box-shape. Therefore, the outline of the object is recognized by determining the positions of the upper, lower, left, and right ends of the object. The process of recognizing the outline of the object will be described with reference to <FGREF>FIGS. 13 and 14</FGREF>.
<FGREF>FIG. 13</FGREF> shows a flowchart of recognizing the outline of the object. FIG. 14(A) shows an example of a captured image. FIGS. 14(B) and (C) show a binary image showing horizontal and vertical edges extracted from the image in FIG. 14(A) and judged to belong to the vehicle ahead by horizontal and vertical extraction parts 7 and 9 respectively.
Referring to <FGREF>FIG. 1</FGREF> again, a lane line detection part 23 detects lane lines of the vehicle mounting the system of the invention from the captured image stored in the image memory 2. In the present embodiment, the lane lines are defined as lines delimiting the lane in which the vehicle mounting the system of the invention is running from other lanes.
The method for detecting the road area within a captured image in accordance with the method disclosed in the above application will be described below by referring to <FGREF>FIGS. 15 and 16</FGREF>. <FGREF>FIG. 15</FGREF> is a block diagram illustrating in detail the lane line detection part 23. FIG. 16(A) shows an example of a captured image in which the vehicle ahead 40 and lane lines 140 are included. For convenience in computation, the y-axis is fixed in the vertical direction and the x-axis is fixed in the horizontal direction to represent the position of each of pixels within the image by x and y coordinates.
After the intensity values of pixels corresponding to markings on the road surface have been excluded, the intensity extraction part 31 determines a reference intensity value by averaging intensity values of the remaining pixels in the area 36 and stores the reference intensity value in an intensity memory 32. The intensity values are expressed digitally with 256 gradations (ranging from pure black “0” to pure white “255”).
The road area judgment part 34 judges a road area based on the comparison results passed from the intensity comparison part 33. If the difference in the intensity values is within a predetermined range (for example, a range having the reference intensity value ±3 may be used), the pixel in question is judged to belong to the road area. If the difference in the intensity values is not within the predetermined range, the pixel in question is determined to belong to a different physical object or to a marking on the road. This is because the intensity values of pixels belonging to the road area are similar each other, but are different from the intensity values of pixels belonging to the vehicle ahead or a white line. The intensity value of the pixel judged to be the road area is stored in the intensity memory part 32 as anew reference value.
Referring to <FGREF>FIG. 1</FGREF> again, a relative position determination part 25 determines the relative position of the vehicle ahead to the lane lines of the vehicle mounting the system of the invention based on the positions of the lane lines determined by the lane line detection part 23 and the object outline determined by the object recognition part 21. Thus, the relative position determination part 25 judges whether the object is outside or inside the lane line of the vehicle, or is traversing the lane line.
With reference to FIGS. 18(A) through 18(D), a method for determining the relative position of the vehicle ahead to the lane lines will be described. FIG. 18(A) shows an image captured by the image sensor 1, in which a vehicle ahead 40 and lane lines 140 are included. As shown in <FGREF>FIG. 18</FGREF> (A), the x and y axes are fixed for the image. In FIG. 18(B), the black rectangle surrounding the vehicle ahead 40 indicates the outline 100 determined by the object recognition part 21. As described above, the outline 100 specify Y1, Y2, X1, and X2, which are the positions of the upper, lower, left, and right ends of the vehicle ahead 40 respectively. In FIG. 18(C), the bold lines indicate the lane lines 150 determined by the lane line detection part 23. Each of the lane lines 150 is represented as a function of x and y.
FIG. 18(D) shows the relationship between the outline 100 of the vehicle ahead 40 and the lane line 150. The relative position determination part 25 determines the intersections B1 and B2 between the horizontal line corresponding to Y2 and each of the lane lines 150 respectively. The coordinates of the determined intersections are defined as (XB1, Y2) and (XB2, Y2) respectively. The relative position determination part 25 then compares the positions in horizontal direction of the intersections XB1 and XB2 with the positions of the left end X1 and right end X2 of the vehicle ahead 40. If X1≧;XB1 and X2≦;XB2, then the relative position determination part 25 judges that the vehicle ahead 40 is within the lane of the vehicle. The rule for determining the relative position of the vehicle ahead to the lane lines is shown in Table 1.
<TABLE-US><TABLE-CALS><table><tgroup><colspec></colspec><colspec></colspec><colspec></colspec><thead><row><entry></entry><entry>TABLE 1</entry></row><row><entry></entry><entry></entry></row><row><entry></entry><entry>Relationship between left and</entry><entry></entry></row><row><entry></entry><entry>right ends of the vehicle ahead</entry><entry>Relative position of the</entry></row><row><entry></entry><entry>and the intersections</entry><entry>vehicle ahead to the lane lines</entry></row><row><entry></entry><entry></entry></row></thead><tbody><row><entry></entry><entry>X2 < XB1</entry><entry>The vehicle ahead is outside</entry></row><row><entry></entry><entry></entry><entry>the left lane line</entry></row><row><entry></entry><entry>X1 < XB1 AND XB1 < X2</entry><entry>The vehicle ahead is</entry></row><row><entry></entry><entry></entry><entry>traversing the left lane line</entry></row><row><entry></entry><entry>X1 ≧; XB1 AND X2 ≦; XB2</entry><entry>The vehicle ahead is between</entry></row><row><entry></entry><entry></entry><entry>the left and right lane lines</entry></row><row><entry></entry><entry>X1 < XB2 AND X2 > XB2</entry><entry>The vehicle ahead is</entry></row><row><entry></entry><entry></entry><entry>traversing the right lane line</entry></row><row><entry></entry><entry>X1 > XB2</entry><entry>The vehicle ahead is outside</entry></row><row><entry></entry><entry></entry><entry>the right lane line</entry></row><row><entry></entry><entry></entry></row></tbody></tgroup></table></TABLE-CALS></TABLE-US>
For the purpose of comparison, FIG. 20(B), which is the same image as <FGREF>FIG. 20</FGREF> (A), shows the relative position of the vehicle ahead to the lane lines in accordance with a conventional method using a radar apparatus. As seen in FIG. 20(B), the position of the vehicle ahead 40 can be obtained according to the position data 200 detected by the radar apparatus, but the size of the vehicle ahead cannot be recognized. Therefore, when the predetermined size for the vehicle ahead to be recognized is stored beforehand as indicated by the rectangle with dotted lines as shown in FIG. 20(B), the vehicle ahead may be erroneously recognized as running across the lane line.
Referring to <FGREF>FIG. 1</FGREF> again, the relative position determined by the relative position determination part 25 is passed to a vehicle controller 19, which controls the operation of the vehicle mounting the system of the invention according to the received relative position. For example, when the vehicle ahead enters the lane line of the vehicle, the driver may be notified of the entrance by a warning voice message, a warning lamp, or a warning beep. If the distance to the vehicle ahead is smaller than a predetermined value, then the engine of the vehicle mounting the system of the invention may be controlled to forcibly decelerate.
Each of the processing area setting part 5, the horizontal edge extraction part 7, the vertical edge extraction part 9, the horizontal edge memory 12, the vertical edge memory 14, the object outline recognition part 15, the intensity extraction part 31, the intensity comparison part 33, the road area determination part 34, the lane line recognition part 35, the relative position determination part 25, the vehicle controller 19, the image memory 2, the object position memory 4, and the intensity memory 32 shown in <FGREF>FIGS. 1</FGREF>, 2, 15, and 17 can be implemented by a micro-controller which typically includes a central processing part (CPU), a read-only memory storing control programs and data, and a random-access memory (RAM) providing a working area for the CPU and temporary storage for various data. In other words, computer programs stored in the ROM implements the above-described functions of the functional blocks shown in FIG. 1.
Inoue, Shigeru, Izawa, Hisaya, Saka, Masakazu, Aoki, Tomoyoshi
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