Source: http://www.google.com/patents/US6853738?ie=ISO-8859-1
Timestamp: 2014-09-23 12:25:18
Document Index: 404671627

Matched Legal Cases: ['art 33', 'art 31', 'art 43', 'art 43', 'art 36', 'art 41', 'art 42', 'art 43', 'art 36', 'art 36', 'art 36', 'art 36', 'art 41', 'art 42', 'art 43', 'art 36', 'art 41', 'art 42', 'art 42', 'art 44', 'art 37']

Patent US6853738 - Optical object recognition system - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign in<nobr>Advanced Patent Search</nobr>PatentsAn object recognition system comprises a memory for storing a plurality of distance ranges with different distance labels associated with respective distance ranges. The controller converts measured distance values into distance labels according to distance ranges to which the distance values belong....http://www.google.com/patents/US6853738?utm_source=gb-gplus-sharePatent US6853738 - Optical object recognition systemAdvanced Patent SearchPublication numberUS6853738 B1Publication typeGrantApplication numberUS 09/572,249Publication dateFeb 8, 2005Filing dateMay 17, 2000Priority dateJun 16, 1999Fee statusPaidAlso published asDE10029423A1, DE10029423B4Publication number09572249, 572249, US 6853738 B1, US 6853738B1, US-B1-6853738, US6853738 B1, US6853738B1InventorsMorimichi Nishigaki, Masakazu SakaOriginal AssigneeHonda Giken Kogyo Kabushiki KaishaExport CitationBiBTeX, EndNote, RefManPatent Citations (7), Referenced by (29), Classifications (29), Legal Events (4) External Links: USPTO, USPTO Assignment, EspacenetOptical object recognition systemUS 6853738 B1Abstract An object recognition system comprises a memory for storing a plurality of distance ranges with different distance labels associated with respective distance ranges. The controller converts measured distance values into distance labels according to distance ranges to which the distance values belong. The controller groups the sections or windows of a captured image based on assigned distance labels. Detection area or viewing area of the sensors is divided into a plurality of distance ranges according to tolerance of the measured distance such that broader distance range is defined as the distance from the system is larger. The controller scans the windows with distance labels using a template that defines a joining relationship for the windows and assigns each window with a cluster label that is a combination of the distance label and a occurrence indicia, which is the same for the windows that satisfy the joining relationship. The controller unites the windows having the same cluster labels into a cluster, generates three dimensional data of each of said clusters and combines the clusters that are positioned close to each other based on the three dimensional data to form a candidate of a physical object. The system includes a memory for storing three dimensional data of one or more physical objects that were recognized in previous recognition cycle. The controller infers a physical object which would be recognized in the current cycle based on the stored data and a speed of the vehicle relative to the physical object. The controller compares the inferred physical object with said candidate of a physical object to recognize a physical object.
Furthermore, when the distance ranges in which clustering is performed are fixed as in the device described in Japanese Patent Application Kokai No. Hei 9-79821, precision of the distance value drops as the distance becomes larger. Generally, the calculation of a distance �d� is expressed by the formula �d=C/s (C is a constant)�, where �s� is a parallax. While the parallax resolution is constant, the distance resolution drops as the distance becomes larger. As a result, when a physical object is imaged over a plurality of windows, larger error is generated in distance values for respective windows as the distance becomes larger.
The term �estimated distance� refers to the distance to the road surface when the vehicle is parallel to the road surface. For example, such estimated distances are calculated beforehand based on the attachment positions, installation angles, base line length, focal lengths and size of the image sensor 3 and 3′ (realized by means of CCD arrays) and the positions of the windows in the image, and are stored in an estimated distance memory 32.
The distance ranges are set in accordance with the tolerance in the measured distances. Here, the value of the distance tolerance depends on the specifications, etc., of the image sensor 3 and 3′. For example, assuming that the tolerance in the measured distances is 10% or less of the actual distances, the distance range for a certain given distance can be set as �distance�(distance�(1+0.1))�. In the present embodiment, because a precision of 10% tolerance may not be insured for all the pixels, the distance ranges are set with the distance tolerance of 30% for high speed processing. Accordingly, the distance range for a certain given distance is set as �distance�(distance�(1+0.3))�.
FIG. 5 is a table showing the relationship between distances and the distance labels where the tolerance is set at 30%. Different distance labels are provided to different distance ranges. For example, in the case of a distance of 1 m, 30% of the distance is 0 (values below the decimal point are discarded). Accordingly, the distance label of �1� is assigned to the distance 1 m. In the case of a distance of 2 m, because 30% of the distance is 0, the label of �2� is assigned to the distance 2 m. Likewise, the label of �3� is assigned to distances 3. For the distance 4, because 30% of the distance is 1 m, the label of 4 is assigned to the distance range from 4 m to 4+1=5 m. Likewise, for the distance 6, because 30% of the distance is 1 m, the label of 5 is assigned to the distance range from 6 m to 6+1=7 m.
For the distance 8 m, because 30% of the distance is 2 m, the next label of 6 is assigned to distance range from 8 m to 8+2=10 m. Here, the distance label increments by 1 each time the distance range changes. In the case of a distance of 20 m, 30% of the distance is 6 m. Accordingly, a distance label of �9� is assigned to the distance range of 20 m through 26 m. In this way, the distance ranges are progressively set from short distances to long distances, so that the detection area 100 is divided into a plurality of distance ranges. Alternatively, several distance ranges with different distance labels may be combined to form a single distance range.
Referring to FIG. 1, the clustering part 33 performs clustering of the windows that remain undeleted by the road surface exclusion part 31. First, the distance converter 26 converts the measured distances into distance labels for respective windows. The measured distances of the windows are checked referring to the distance conversion table stored in the distance range memory 9 and converted into the distance labels. Here, in the case of windows for which no measured distance has been obtained because of for example lack of contrast, a label not used in the distance conversion table (for example a label �0�) may be assigned. When the distance converter 26 converts the measured distances into distance labels, the distance converter 26 counts and calculates the occurrence degree of the windows in respective distance ranges.
When the occurrence degree of windows having a certain distance label is less than a certain threshold value after a conversion to distance labels has been performed for all of the windows, the distance converter 26 sets the distance labels to �0�. This action is suited for achieving high-speed operation. Specifically, because it is determined that no effective physical object (vehicle running in front, etc.) has been captured in the distance range in question, the distance labels are reset to �0�. Different values can be set as the threshold value for each distance range.
In the example shown in FIG. 6(b), windows with a distance label of �1� consist only of the single window W31, and windows with a distance label of �2� consist of the five windows W53, W73, W66, W68 and W48. If the threshold value for the distance labels �1� and �2� is set at for example �10�, the distance labels of these windows are replaced with �0�. The distance labels obtained as a result are shown in FIG. 6(c).
The cluster labeler 27 assigns cluster labels to the windows using the template shown in FIG. 7. T1 through T5 in FIG. 7(a) indicate positions in the template. �a� through �e� in FIG. 7(b) indicate the distance labels of windows respectively corresponding to the positions T1 through T5 when the template is positioned so that T4 assumes the place of a window to be processed. �A� through �E� in FIG. 7(c) indicate the cluster labels assigned to windows respectively corresponding to the positions T1 through T5.
The table in FIG. 7(d) shows the type of cluster label D that is assigned to the window at position T4 based on the distance labels for the windows at positions T1 through T5 when T4 is placed at the window to be processed. For example, if the distance labels �a� through �e� at positions T1 through T5 satisfy condition 5 in FIG. 7(d), then a cluster label B is assigned to the window at T4. The cluster label �L� is assigned when conditions 2 or 3 is satisfied requiring a new cluster label. The cluster labeler 27 successively scans the windows on the image placing T4 of the template at respective windows on the image, thus assigning cluster label D to respective windows. The table of FIG. 7(d) shows the cluster labels given to respective windows in accordance with the above described scheme and additional scheme to be described below.
Referring to FIG. 8, respective windows are identified in terms of W11 through W44 as shown in the table in FIG. 8(f). The measured distances of the windows have been converted into distance labels as shown in FIG. 8(a) by the distance converter 26. The cluster labeler 27 scans the windows from the upper left to the lower right. Since the distance label of the window W11 is �0�, condition 1 of the table in FIG. 7(d) is satisfied, and a cluster label of �0� is assigned to the window W11. Similarly, the cluster label of �0� is assigned to each of the windows W12 through W14 and W21.
The window 22 that has a distance label �6� satisfies condition 2 of the table in FIG. 7(d), and a new cluster label �61� is assigned (FIG. 8 (b)). In this example, the cluster label is expressed by two digits. The higher digit indicates the distance label and the lower digit indicates occurrence number of windows having the same distance label. The second digit is incremented by one each time conditions 2 or 3 of the table in FIG. 7(d) is satisfied for the window having the same distance label. Any symbols such as numerals and alphabetic characters may also be used as the cluster labels.
Next, the template is placed to align T4 with the window W23. Here, since the window W23 satisfies condition 4 of the table in FIG. 7(d), the same cluster label as that of window W22, i. e., the cluster label �61�, is assigned to the window W23 (FIG. 8(c)). Since the windows W24 and W31 satisfy condition 1, a cluster label of �0� is assigned to these windows. The window W32 satisfies condition 6 of the table in FIG. 7(d), and the same cluster label as that of the window W22, i. e., the cluster label �61� is assigned to the window W32 (FIG. 8(d)). The window W33 satisfies condition 7 of the table in FIG. 7(d), the same cluster label as that of the window W23, i. e., the cluster label �61�, is assigned to the window W33. The rest of the windows W34 and W41 through W44 satisfy condition 1, and cluster label of �0� are assigned to these windows. In this way, cluster labels are given as shown in FIG. 8(e).
Referring to FIG. 1, the cluster labeler 27 moves the template across the window on the image from the upper left to the lower right and assigns cluster labels to the windows based on the distance labels in FIG. 9(a) by the same method as that illustrated in FIG. 8. The windows W11 through W15, W21 through W23 satisfy condition 1, and cluster labels of �0� are assigned to these windows. Since the window W24 satisfies condition 2 in FIG. 7(d) and this is the first occurrence of a window having the distance label of 6, a new cluster label of �61� is assigned to this window as shown in FIG. 9(b). Since the windows W25 and W31 satisfy condition 1, cluster labels of �0� are assigned to these windows. Window W32 satisfies condition 2 and this is the second occurrence of a window having the distance label of 6, a new cluster label of �62� is assigned (FIG. 9(c)). Since the windows W33 satisfies condition 4, cluster label of �62� which is the same as that of the window W32 is assigned (FIG. 9(d)).
Window W34 satisfies condition 8 of the table in FIG. 7(d), and a cluster label of �61� which is the same as that of the window W24 is assigned to window W34 (FIG. 9(e)). As a result, the cluster labels of the windows W32 and W33 are different from the cluster labels of the windows W24 and W34, despite that the windows are adjacent to each other.
When condition 8 of the table in FIG. 7(d) is satisfied, the cluster labels corresponding to A and C of the template are linked. The cluster labels �62� and �61� of windows W33 and W24 in this example are linked and stored in the cluster memory 48 as an integral cluster. After cluster labels have been assigned to all of the windows, the same cluster label replaces the two cluster labels stored in linked form. For example, the cluster label �62� may be replaced by �61� or vice-versa. Furthermore, for example, �61� and �62� may be replaced by a new cluster label �63�. FIG. 9(f) shows cluster labels thus assigned to respective windows.
Referring to FIG. 1, three-dimension converter 35 generates three-dimensional data of the clusters. As shown in FIG. 10, the three-dimensional information includes three coordinates in the present embodiment, i. e., horizontal position (x), vertical position (y) and road surface distance (z). The �x� coordinate expressing the horizontal position corresponds to the direction in which the columns of the windows are lined up (see FIG. 3(b)). The �y� coordinates that expresses the vertical position corresponds to the direction of height from the road surface. The z coordinate indicating the distance of the road surface corresponds to the direction in which the rows of the windows are lined up (see FIG. 3(b)). The �z� coordinate is proportional to the measured distance �d�.
The origin O indicates that point of the road surface where the vehicle is located. The �x�, �y� and �z� axes intersect at right angles at the origin O. The �x� axis extends to the left and right as seen from the vehicle. The �y� axis extends in the direction perpendicular to the road surface and the �z� axis in the direction of advance of the vehicle. The imaging camera 53 is located at a height �H� in the direction of the �y� axis from the origin O. The physical object 54 has a height �h� and a width of �g�, and is located at a distance �i� in the direction of the �z� axis. If the physical object 54 is not present, then the point 55 on the road surface is included in the image captured by the imaging camera 53. If the physical object 54 is present on the road, the window that would include the image of point 55 will include a point 56 of the physical object instead of the image of point 55 of the road surface. The estimated distance �D� is the distance between the imaging camera 53 and the point 55 on the road surface. When no physical object 54 is present, this estimated distance �D� is equal to the measured distance to the captured point 55. In FIG. 10, the measured distance �d� is the distance from the imaging camera 53 to the point 56 of the physical object 54, which is calculated by the method described above with reference to FIG. 2. In the (x, y, z) coordinate system, the position of the imaging camera 53 is (O, H, O) and the position of point 56 is (g, h, i).
Since the estimated distance �D� for each window and the height �H� of the imaging camera from the estimated road surface are fixed values, they can be calculated beforehand and stored. As is clear from FIG. 10, the height �h� of the object can be determined from the following equation (1), and distance �i� to the object 54 can be determined from the following equation (2).
The horizontal distance from the vehicle that is the distance in the �x� axis in FIG. 10 is determined beforehand for location of windows based on the position of the imaging camera. For example, the third row and the third column of windows indicates positions 1 meter to the left from the center of the vehicle. Accordingly, the value of the �x� coordinate of point 56 (in the present example, this is g, and is equal to the value of the width of the object of imaging) can be determined based on the position of the window that includes point 56. Thus, the respective windows forming clusters can be expressed in terms of x, y and z coordinates. In another embodiment, it would also be possible to use (for example) the measured distance �d� instead of the �z� coordinate indicating the road surface distance, and windows could also be expressed using a different coordinate system from the coordinate system described above.
Distance of two clusters=(d 1�w 1+d 2+w 2)/(w 1�w 2) (3)
The differences dx and dy in the horizontal positions and vertical positions of the two clusters are expressed as the spacing of the two clusters, and the difference in distance dz is expressed as the difference in the distances of the respective clusters (d1 and d2 in the above description). For example, FIG. 11(a) shows a plurality of clusters as seen from the x-y plane, and FIG. 11(b) shows the same clusters as those in FIG. 11(a), as seen from the x-z plane. The difference in the horizontal positions of the clusters C4 and C6 is expressed by dx in the direction of the �x� axis, and the difference in the vertical positions is expressed by dy in the direction of the �y� axis. If the distances of the clusters C4 and C6 from the vehicle are respectively d4 and d6, then the difference in distance is expressed by dz in the direction of the z axis.
30.0� 8.0
(position of previous physical object+relative speed�detection time interval)
The first recognition part 43 successively compares the attributes of combined clusters which have corresponding inferred physical objects with the attributes of the inferred physical objects. The recognition part 43 recognizes the combined clusters that have attributes closest to the attributes of the inferred physical objects as physical objects. Here, the attributes used are distance, horizontal position, vertical position, width and height, and the comparison of attributes is accomplished using the following Equation (4). The meanings of the variables in Equation (4) are shown in Table 3. E1 = ( Xc - Xt ) 2 + ( Yc - Yt ) 2 + ( Zc - Zt ) 2 / C � Zt +  Wc - Wt  +  Hc - Ht  ( 4 ) TABLE 3
In cases where for example a certain cluster overlaps with a plurality of inferred physical objects and thus represents only portions of the inferred physical objects, such a cluster may be excluded from combined clusters. The clusters C22 through C26 and the inferred physical object 75 are stored in the cluster memory 48 and estimate physical object memory 49 respectively, and a �process completed� flag is set.
The process performed by the cluster grouping part 36, cluster selection part 41, candidate generating part 42 and first and second recognition part 43 and 44 is repeated until all the clusters are processed and �processing completed� flags are set. Specifically, the cluster grouping part 36 checks the �processing completed� flags of the clusters stored in the cluster memory 48, and when all clusters are given the �processing completed� flags, the process completes. Alternatively, it would also be possible to arrange the system so that an upper limit (e. g., 4) is set beforehand on the number of objects to be recognized as physical objects, and repetition of the recognition process completes when the number of recognized physical objects reaches this number.
As mentioned above with reference to FIG. 12(f), after the physical object 78 has been recognized, the cluster grouping part 36 checks the �processing completed� flags of the clusters stored in the cluster memory 48 and fetches cluster C21 and clusters C27 through C31 for which no �processing completed� flags have been set. The cluster grouping part 36 checks the �processing completed� flags of the inferred physical objects stored in the inferred physical object memory 49, and extracts the inferred physical object 76 for which no �processing completed� flag has been set.
Since the difference in the horizontal positions of the cluster C21 and those of the clusters C27 through C31 exceed the threshold value, the cluster grouping part 36 generates two different cluster groups. Assuming that the cluster group formed from the clusters C27 through C31 is nearer to the vehicle, the cluster selection part 41 first selects the cluster group consisting of the clusters C27 through C31, and selects inferred physical object 76, whose distance from the vehicle is substantially the same as that of the cluster group C27 through C31. �Substantially the same� means that the difference in the distance is smaller than than the threshold value. Note that the inferred physical object 76 partially overlaps in the horizontal and vertical positions with the cluster group C27 through C31. The clusters C27 through C31 that overlaps with the inferred physical object 76 are selected among the clusters forming the cluster group.
The candidate generating part 42 determines combined clusters from combinations of the clusters C27 through C31. The first recognition part 43 compares the attributes of the respective combined clusters with the attributes of the inferred physical object 76. As a result, the combined cluster consisting of the clusters C27 through C31 is determined to have attributes that are the closest to those of the inferred physical object 76 so that the combined cluster consisting of the clusters C27 through C31 is recognized as a physical object 79 (FIG. 12(f)). The clusters C27 through C31 recognized as a physical object and the corresponding inferred physical object 76 are stored with �processing completed� flags in the cluster memory 48 and inferred physical object memory 49 respectively.
Next, the cluster grouping part 36 fetches from the cluster memory 48 the cluster C21 for which no �processing completed� flag has been set. Since this is a single cluster, the cluster C21 is treated as a cluster group. In this example, all the inferred physical objects have been processed so that there is no corresponding inferred physical object to be compared. Accordingly, the cluster selection part 41 selects the cluster C21 and transfers it to the candidate generating part 42. The candidate generating part 42 determines combined clusters from combinations of all of the clusters contained in a cluster group. Since the cluster C21 is a single cluster, C21 is treated as a combined cluster. The combined cluster consisting of cluster C21 is processed by the second recognition part 44 via the judging part 37.
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