Follow-Based Forward Obstacle Detection Using Vision Insensitive Feature for Road Cycling

Abstract:

Paper proposes a method for detecting general obstacles on a road by subtracting present and past in road cycling camera images. The image-subtraction-based object detection approach can be applied to detect any kind of obstacles although the existing learning based methods detect only specific obstacles. To detect general obstacles, the proposed method first computes a frame-by-frame correspondence between the present and the past in-road cycling camera image sequences, and then registries road surfaces between the frames. Finally, obstacles are detected by applying image subtraction to the redistricted road surface regions with a vision insensitive feature for robust detection. Experiments were conducted by using several image sequences captured by an actual in-road cycling camera to confirm the effectiveness of the proposed method. The experimental results shows that the proposed method can detect general obstacles accurately at a distance enough to avoid them safely even with different situations.

• Periodical: Advanced Materials Research (Volumes 718-720)
• Pages: 2427-2431
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.718-720.2427
• Online since: July 2013
• Authors: Jing Yang, Ming Gou
• Keywords: Obstacle Detection, Road Cycling, Vision Insensitive Feature

References

[1] Boehm, J.: Multi-image fusion for occlusion-free facade texturing. Int. Archives of Photogrammetric, Remote Sensing and Spatial Information Sciences 35, 867–872 (2004)

[2] Zabih, R., Woodfill, J.: Non-parametric Local Transforms for Computing Visual Correspondence. In: Eklundh, J.-O. (ed.) ECCV 1994, Part II. LNCS, vol. 801, p.151–158. Springer, Heidelberg (1994)

DOI: 10.1007/bfb0028345

[3] Nishida, K., Kurita, T.: Boosting with cross-validation based feature selection for pedestrian detection. In: Proc. 2008 IEEE World Congress on Computational Intelligence, p.1251–1257 (2008)

DOI: 10.1109/ijcnn.2008.4633959

[4] Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. on Image Processing 19, 1635-1650 (2010)

DOI: 10.1109/tip.2010.2042645