Path Recognition Technology of Self-Tracing Car Based on OV6620

Shuang Cheng; Kai Shi; Li Guang  Wang; Meng Jun Ye; Chang Hui Hu

doi:10.4028/www.scientific.net/AMR.760-762.1572

Paper Titles

The Location of Cancer Area Based on Region Growing Algorithm in Medical Image
p.1552

Kinect Depth Data Segmentation Based on Gauss Mixture Model Clustering
p.1556

A Remote Educational Video Dynamic Image Sharp Processing Algorithm
p.1562

Coastline Detection from Remote Sensing Image Based on K-Mean Cluster and Distance Transform Algorithm
p.1567

Path Recognition Technology of Self-Tracing Car Based on OV6620
p.1572

Iris Boundary Localization Based on Improve Hough Transform
p.1576

Indoor Passive Millimeter-Wave Imaging for Concealed Object Detection
p.1581

Impervious Surface Information Extracting Approach Using Landsat TM Remote Sensing Image
p.1585

Medical Image Segmentation
p.1590

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 760-762Path Recognition Technology of Self-Tracing Car...

Path Recognition Technology of Self-Tracing Car Based on OV6620

Abstract:

This paper proposes extracting black line with small-scale method in the self-tracing car system based on OV6620 as the video sensor unit. Describe the hardware design of OV6620 and other two kinds of image signal extracting methods, which are to extract black line from left to right, and extract black line from middle to both sides. The testing shows that extracting black line with small-scale method has good stability, accuracy, and anti-interference.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 760-762)

Pages:

1572-1575

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.760-762.1572

Citation:

Cite this paper

Online since:

September 2013

Authors:

Shuang Cheng, Kai Shi, Li Guang Wang, Meng Jun Ye, Chang Hui Hu

Keywords:

Extract Black Line from Left to Right, Extract Black Line from Middle to both Sides, Extract Black Line with Small-Scale, OV6620, Self-Tracing Car

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] WANG Zi-hui YE Yun-yue. Research on Image Acquisition of the self-Tracing Car Based on CMOS Camera Sensor. Chinese Journal of Sensor and Actuators. 2009. 4: 484-487, In Chinese.

Google Scholar

[2] HU Chang-hui YE Meng-jun, et al. Research of Smart Car Path Recongnition Based on Electromagnetism Technology. Journal of Hubei Normal University(Natural Science). 2011. 2: 54-58, In Chinese.

Google Scholar

[3] A. Sharma, K. K. Paliwal and Seiya Imoto, Principal component anlysis using QR decomposition, Int.J. Mach. Learn. &Cyber, DOI 10. 1007/s13042-012-0131-7.

DOI: 10.1007/s13042-012-0131-7

Google Scholar

[4] R.H. Li, Eddie, C.L. Chan and G. Baciu, DLDA and LDA/QR Equivalance Framework for Human Face Recognition, Proc. 9th IEEE Int. Conf. on Cognitive Informatics (ICCI'10), pp.180-185, (2010).

DOI: 10.1109/coginf.2010.5599743

Google Scholar

[5] K. K. Pailwal and A. Sharma, Improved direct LDA and its application to DNA microarray gene expression data, Pattern Recognition Letters 31 (2010)2489-2492.

DOI: 10.1016/j.patrec.2010.08.003

Google Scholar

[6] F. X. Song, D. Zhang, J. Z Wang, H. Liu and Q. Tao, A parameterized direct LDA and its application to face recognition, Neurocomputing 71 (2007)191-196.

DOI: 10.1016/j.neucom.2007.01.003

Google Scholar

[7] 0v6620Datasheet. OmniVision Technologies, Inc. http: /www. ovt. com.

Google Scholar