Object detection apparatus and method

• US 20050105771A1
• Filed: 08/30/2004
• Published: 05/19/2005
• Est. Priority Date: 09/02/2003
• Status: Abandoned Application

• Current Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Original Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Inventors: Kondo, Hiroshhi, Ido, Tetsuya, Miura, Atsushi, Tsujino, Hiroshi, Koshizen, Takamasa, Akatsuka, Koji, Nagai, Shinichi
• Application Number:
  US10/930,566
• Publication Number:
  US 20050105771A1
• Time in Patent Office: Days
• US Class Current: 382/103
• CPC Class Codes: G06T 1/0014 Image feed-back for automatic industrial control, e.g. robot with camera robots B25J19/023
• CPC Class Codes: G06T 7/215 Motion-based segmentation
• CPC Class Codes: G06V 10/255 Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
• CPC Class Codes: G06V 10/449 Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters

Abstract

The object detection apparatus according to the invention detects an object based on input images that are captured sequentially in time in a moving unit. The apparatus generates an action command to be sent to the moving unit, calculates flow information for each local area in the input image, and estimates an action of the moving unit based on the flow information. The apparatus calculates a difference between the estimated action and the action command and then determines a specific local area as a figure area when such difference in association with that specific local area exhibits an error larger than a predetermined value. The apparatus determines presence/absence of an object in the figure area.

Claims

1. An object detection apparatus for detecting an object based on input images that are captured sequentially in time by a moving unit, comprising:

an action generating section for generating an action command to be sent to the moving unit;

a local image processor for calculating flow information for each local area in the input image;

a figure-ground estimating section for estimating an action of the moving unit based on the flow information, calculating a difference between the estimated action and the action command and then determining a certain local area as a figure area when such difference in association with that specific local area exhibits an error larger than a predetermined value,; and

an object presence/absence determining section for determining presence/absence of an object in the figure area.

2. The object detection apparatus as claimed in claim 1, further comprising an object recognizing section for recognizing an object when it is determined that an object exists in the figure area.

3. The object detection apparatus as claimed in claim 1, wherein the figure-ground estimating section estimates the action of the moving unit by utilizing learning results of the relation between the flow information for each local area and the action of the moving unit.

4. The object detection apparatus as claimed in claim 3, wherein the flow information fro each local area and the action of the moving unit is related through a neural network.

5. The object detection apparatus as claimed in claim 4, wherein the figure-ground estimating section propagates back the difference between the estimated action and the action command by using an error back-propagation algorithm so as to determine the local area that causes the error.

6. The object detection apparatus as claimed in claim 5, wherein the figure-ground estimating section determines that an abnormality occurs in the moving unit or in the environment surrounding the moving unit when an extent occupied by the figure areas causing the error exceeds a predetermined threshold value.

7. The object detection apparatus as claimed in claim 5, wherein the figure-ground estimating section removes the areas causing the difference between the estimated action and the action command from the flow information for each local area and estimates again an action of the moving unit from the remaining flow information.

8. The object detection apparatus as claimed in claim 1, wherein the object presence/absence determining section compares frequency elements of sequential images in the figure areas each other after removing the high-frequency elements from those frequency elements so as to determine presence or absence of continuity which is a measurement for evaluating succession of an object in the images and then determines that an object is included in the figure areas when the presence of the continuity is determined.

9. An object detection method, wherein frequency elements of sequentially-captured images after removing the high-frequency elements from those frequency elements are compared each other to determine presence or absence of continuity which is a measurement for evaluating succession of an object in the images and then it is determined that the same object is included in the images when the presence of the continuity is determined.

10. An object detection method for detecting an object based on input images that are captured sequentially in time by a moving unit, including steps of:

generating and sending an action command to the moving unit;

calculating flow information for each local area in the input image;

estimating an action of the moving unit based on the flow information;

comparing the estimated action with the action command to calculate a difference between them;

determining a specific local area as a figure area when such difference in association with that specific local area exhibits an error larger than a predetermined value; and

determining presence/absence of an object in the figure area.

11. The object detection method as claimed in claim 10, further including a step of recognizing an object when it is determined that an object exists in the figure area.

12. The object detection method as claimed in claim 10, further including a step of estimating the action of the moving unit based on learning results of the relation between the flow information for each local area and the action of the moving unit.

13. The object detection method as claimed in claim 12, wherein the flow information fro each local area and the action of the moving unit is related through a neural network.

14. The object detection method as claimed in claim 13, wherein the difference between the estimated action and the action command is propagated back by using an error back-propagation algorithm so that the local area causing the error is determined.

15. The object detection method as claimed in claim 10, wherein it is determined that an abnormality occurs in the moving unit or in the environment surrounding the moving unit when an extent occupied by the figure areas causing the error exceeds a predetermined threshold value.

16. The object detection method as claimed in claim 10, further including a step of removing the areas causing the difference between the estimated action and the action command from the flow information for each local area and estimating again an action of the moving unit from the remaining flow information.

17. A computer program product for an object detection apparatus including a computer for detecting an object based on input images that are captured sequentially in time by a moving unit, said program when executed performing the functions of:

generating and sending an action command to the moving unit;

calculating flow information for each local area in the input image;

estimating an action of the moving unit based on the flow information;

comparing the estimated action with the action command to calculate a difference between them;

determining a specific local area as a figure area when such difference in association with that specific local area exhibits an error larger than a predetermined value; and

determining presence/absence of an object in the figure area.

18. The computer program product as claimed in claim 17, further performing the function of recognizing an object when it is determined that an object exists in the figure area.

19. The computer program product as claimed in claim 17, further performing the function of estimating the action of the moving unit utilizing learning results of the relation between the flow information for each local area and the action of the moving unit.

20. The computer program product as claimed in claim 19, wherein the flow information fro each local area and the action of the moving unit is related through a neural network.