A Study on Computational Time Reduction of Road Obstacle Detection by Parallel Image Processor

Yutaro Okamoto; Chinthaka Premach; ra; Kiyotaka Kato

doi:10.20965/jaciii.2014.p0849

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JACIII Vol.18 No.5 pp. 849-855

doi: 10.20965/jaciii.2014.p0849

(2014)

Paper:

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A Study on Computational Time Reduction of Road Obstacle Detection by Parallel Image Processor

Yutaro Okamoto, Chinthaka Premachandra, and Kiyotaka Kato

Department of Electrical Engineering, Graduate School of Engineering, Tokyo University of Science,

Received:

May 4, 2014

Accepted:

June 29, 2014

Published:

September 20, 2014

Keywords:

computational time reduction, obstacle detection, parallel image processor, image sample variance, discriminant analysis

Abstract

Automatic road obstacle detection is one of the significant problem in Intelligent Transport Systems (ITS). Many studies have been conducted for this interesting problem by using on-vehicle cameras. However, those methods still needs a dozens of milliseconds for image processing. To develop the quick obstacle avoidance devices for vehicles, further computational time reduction is expected. Furthermore, regarding the applications, compact hardware is also expected for implementation. Thus, we study on computational time reduction of the road obstacle detection by using a small-type parallel image processor. Here, computational time is reduced by developing an obstacle detection algorithm which is appropriated to parallel processing concept of that hardware. According to the experimental evaluation of the new proposal, we could limit computational time for eleven milliseconds with a good obstacle detection performance.

Cite this article as:

Y. Okamoto, C. Premachandra, and K. Kato, “A Study on Computational Time Reduction of Road Obstacle Detection by Parallel Image Processor,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.5, pp. 849-855, 2014.

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References

[1] S. Kim and H. Kim, “Simple and complex obstacle detection using an overlapped ultrasonic sensor ring,” Int. Conf. on Control, Automation and Systems, Oct. 2012, pp. 2148-2152, 2012.
[2] Z. Xu, Y. Zhuang, and H. Chen, “Obstacle detection and road following using laser scanner,” Proc. of 6th World Congress on Intelligent Control and Automation, pp. 8630-8634, Jun. 2006.
[3] C. Yu and D. Zhang, “Obstacle detection based on a four-layer laser radar,” Proc. of 2007 IEEE Int. Conf. on Robotics and Biometrics, pp. 218-221, Dec. 2007.
[4] W. Mark, J. Heuvel, and F. Groen, “Stereo based obstacle detection with uncertainty in rough terrain,” Proc. of 2007 IEEE Intelligent Vehicles Symp., pp. 1005-1012, Jun. 2007.
[5] N, Intae, “Stereo-based road obstacle detection and tracking,” Proc. of Int. Conf. on Advanced Communication Technology, Feb. 2011, pp. 1181-1184, 2011.
[6] H. Wang, K. Yuan, W. Zou, and Y. Peng, “Real-time obstacle detection with a single camera,” Proc. of 2005 IEEE Int. Conf. on Industrial Technology, Dec. 2005, pp. 92-96.
[7] I. Sato, C. Yamano, and H. Yanagawa, “Crossing obstacle detection with a vehicle-mounted camera,” Proc. of 2007 IEEE Intelligent Vehicles Symp., pp. 60-65, Jun. 2011.
[8] Z. Yankun, H. Chuyang, Weyrich, and Norman, “A single camera based rear obstacle detection system,” Proc. of 2007 IEEE Intelligent Vehicles Symp., pp. 485-490, Jun. 2011.
[9] J. R. Mani, N. D. Gangadhar, and V. K. Reddy, “A Real-time Video Processing Based Driver Assist System,” SASTECH, Vol.9, Issue 1, pp. 9-16, 2010.
[10] M. Noda, T. Takahashi, D. Deguchi, I. Ide, H. Murase, Y. Kojima, and T. Naito, “Recognition of Road Markings in In-Vehicle Camera Images referring to Posture and Speed of the vehicle,” Meeting on Image Recognition and Understanding, 2009.
[11] N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Sys.,Man., Cyber., Vol.9, Issue 1, pp. 62-66, 1979.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] S. Kim and H. Kim, “Simple and complex obstacle detection using an overlapped ultrasonic sensor ring,” Int. Conf. on Control, Automation and Systems, Oct. 2012, pp. 2148-2152, 2012.

[2] [2] Z. Xu, Y. Zhuang, and H. Chen, “Obstacle detection and road following using laser scanner,” Proc. of 6th World Congress on Intelligent Control and Automation, pp. 8630-8634, Jun. 2006.

[3] [3] C. Yu and D. Zhang, “Obstacle detection based on a four-layer laser radar,” Proc. of 2007 IEEE Int. Conf. on Robotics and Biometrics, pp. 218-221, Dec. 2007.

[4] [4] W. Mark, J. Heuvel, and F. Groen, “Stereo based obstacle detection with uncertainty in rough terrain,” Proc. of 2007 IEEE Intelligent Vehicles Symp., pp. 1005-1012, Jun. 2007.

[5] [5] N, Intae, “Stereo-based road obstacle detection and tracking,” Proc. of Int. Conf. on Advanced Communication Technology, Feb. 2011, pp. 1181-1184, 2011.

[6] [6] H. Wang, K. Yuan, W. Zou, and Y. Peng, “Real-time obstacle detection with a single camera,” Proc. of 2005 IEEE Int. Conf. on Industrial Technology, Dec. 2005, pp. 92-96.

[7] [7] I. Sato, C. Yamano, and H. Yanagawa, “Crossing obstacle detection with a vehicle-mounted camera,” Proc. of 2007 IEEE Intelligent Vehicles Symp., pp. 60-65, Jun. 2011.

[8] [8] Z. Yankun, H. Chuyang, Weyrich, and Norman, “A single camera based rear obstacle detection system,” Proc. of 2007 IEEE Intelligent Vehicles Symp., pp. 485-490, Jun. 2011.

[9] [9] J. R. Mani, N. D. Gangadhar, and V. K. Reddy, “A Real-time Video Processing Based Driver Assist System,” SASTECH, Vol.9, Issue 1, pp. 9-16, 2010.

[10] [10] M. Noda, T. Takahashi, D. Deguchi, I. Ide, H. Murase, Y. Kojima, and T. Naito, “Recognition of Road Markings in In-Vehicle Camera Images referring to Posture and Speed of the vehicle,” Meeting on Image Recognition and Understanding, 2009.

[11] [11] N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Sys.,Man., Cyber., Vol.9, Issue 1, pp. 62-66, 1979.