Obstacle detection using v-disparity image

1. Obstacle detection using v-disparity image Based on: Global Correlation Based Ground Plane Estimation Using V-Disparity Image, By: J. Zhao, J. Katupitiya and J. Ward, 2007 Obstacle Detection with Stereo Vision for Off-Road Vehicle Navigation, By: A. Broggi, C. Caraffi, R. I. Fedriga, P. Grisleri, 2005 Real Time Obstacle Detection in Stereovision on Non Flat Road Geometry Through ”V-disparity” Representation, By: R. Labayrade, D. Aubert, J. Tarel, 2002 Presented by: Ali Agha March 25, 2009

2. Motivation • The pitch angle between the cameras and the road surface will change. Therefore, we need to compute the pitch angle and disparity of ground pixels dynamically Obstacle avoidance using stereo vision Points with Z > 0

3. Related work • Disparity map  3D point cloud  Ground plane extraction using plane fitting  obstacle detection • Yu et al (2005) • Disparity map  Optical flow and ground information  obstacle detection • Mascarenhas (2008) • Giachetti et al (1998) • Disparity map  V-disparity image and ground information  obstacle detection • Labayrade (2002) • Broggi (2005) • Zhao (2007)

4. V-Disparity image • Disparity map IΔhas been computed • IvΔ isbuilt by accumulating the pixels of same disparity in IΔ along thev axis u d v v

5. The image of a plane in v-disparity image pin-hole camera model Ground correlation line

6. Ideal and real road and obstacle

7. Hough Transform • Labayrade uses Hough transform to extract the ground correlation line

8. free space on the road

9. Robust ground correlation extraction • Broggi et al. experimentally, found that the ground correlation line during a pitch variation oscillates, parallel to itself. • Zhao et al. investigated this characteristic mathematically and gave the condition for this characteristic to be valid.

10. Condition derived by Zhao et al Θs= is 7.73.

11. Result of this condition • If this condition is satisfied.

12. GLOBAL CORRELATION METHOD • Exploiting this characteristic  Fast and Robust method (even in lack of distinct features) • By accumulating the matching cost (intensity of V-disparity) along each of the candidate lines and choose the one with least (most) accumulated matching cost as ground correlation line.

13. Verifying equations and assumptions(Road has distinct features) • Implementing the Labayrade’s work Disparity map V-D Hough

14. Verifying equations and assumptions(Road has distinct features) • In the sequence of images • the position of ground correlation lines is ranging from 25 to 28. • The slop(g) of ground correlation lines ranges from 5.5 to 5.5652.

15. Results • Applying the method on the same images • Calculate the matching cost (associated with each candidate ground correlation lines) • matching costs associated with candidate lines • Same as Hough transform GCL

16. Without distinct features Disparity map V-D Hough

17. Without distinct features - Comparison • Hough transform and global correlation method GCL Hough

18. Obstacle detection

19. Conclusion • This accuracy is dependant of the image quality and whether or not the ground pixel dominate this area of the image. • It seems an appropriate method for detecting obstacles such as vehicles in road in night using structured light (as the headlight of automobile)

20. Thank youQuestions??