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Pedestrian Protection System for ADAS using ARM 9

by Rajashri Sanatkumar Dixit, S.T. Gandhe, Pravin Dhulekar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 127 - Number 2
Year of Publication: 2015
Authors: Rajashri Sanatkumar Dixit, S.T. Gandhe, Pravin Dhulekar
10.5120/ijca2015906327

Rajashri Sanatkumar Dixit, S.T. Gandhe, Pravin Dhulekar . Pedestrian Protection System for ADAS using ARM 9. International Journal of Computer Applications. 127, 2 ( October 2015), 19-23. DOI=10.5120/ijca2015906327

@article{ 10.5120/ijca2015906327,
author = { Rajashri Sanatkumar Dixit, S.T. Gandhe, Pravin Dhulekar },
title = { Pedestrian Protection System for ADAS using ARM 9 },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 127 },
number = { 2 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 19-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume127/number2/22701-2015906327/ },
doi = { 10.5120/ijca2015906327 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:18:49.637918+05:30
%A Rajashri Sanatkumar Dixit
%A S.T. Gandhe
%A Pravin Dhulekar
%T Pedestrian Protection System for ADAS using ARM 9
%J International Journal of Computer Applications
%@ 0975-8887
%V 127
%N 2
%P 19-23
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

We developed pedestrian protection system by using haar cascade algorithm with Friendly ARM (S3C2440) board as a hardware. This system is developed on opencv platform using ubuntu as an operating system. This system will work in two mode auto and manual. In auto mode as soon as pedestrian get detected break will be applied if pedestrian is in high risk area. And for manual mode there is alarm or buzzer will sound to alert driver. This system gives low false positive as well as low false negative rate. This system is low cost system as compare with state of art.

References
  1. Cortes C, Vapnik V. Support vector machine[J]. Machine learning, 1995, 20(3): 273-297.
  2. Lowe, David G. (1999),” Object recognition from local scale – invariant features”, proceeding of the international conference on computer vision 2.pp.1150-1157.
  3. P. Viola, M. Jones, Robust real-time object detection, Int.J.Comput.Vis. 57(2) (2004)137-154.
  4. P. Viola, M. Jones, and D. Snow, “Detecting Pedestrians Using Patterns of Motion and Appearance,” Int’l J. Computer Vision, vol. 63, no. 2, pp. 153-161, 2005.
  5. Dalal, N. Triggs, B. : Histograms of Oriented Gradients for Human Detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005.
  6. David Geronimo, Antonio M. Lopez, Angel D. Sappa, Member, IEEE, and Thorsten Graf, "Survey of Pedestrian Detection for Advanced Driver Assistance Systems", IEEE Transactions on pattern analysis and machine intelligence, Vol. 32, No. 7, July 2010.
  7. R. Rios-Cabrera, T. Tuytelaars, L. Van Gool ,” Efficient multi-camera detection, tracking, and identification using a shared set of Haar-features”, in: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp.65–71,2011.
  8. Xianbin Cao, Zhong Wang, Pingkun Yan, Xuelong Li, “Rapid pedestrian detection in unseen scenes”,Neurocomputing 74 (2011) 3343–3350.
  9. Jiang-tao Wang, De-bao Chen, Hai-yan Chen, Jing-yu Yang, “On pedestrian detection and tracking in infrared videos”, Pattern Recognition Letters 33 (2012) 775–785.
  10. Lie Guo , Ping-Shu Ge , Ming-Heng Zhang , Lin-Hui Li , Yi-Bing Zhao,” Pedestrian detection for intelligent transportation systems combining AdaBoost algorithm and support vector machine”, Expert Systems with Applications 39 (2012) 4274–4286.
  11. Hulin Kuang, Yanwen Chong, Qingquan Li, Chunhou Zheng,” MutualCascade method for pedestrian detection”, Neurocomputing137(2014)127–135
  12. Chong Yan-Wen, KUANG Hu-Lin, LI Qing-Quan, Two- stage pedestrian detection based on multiple features and machine learning, Acta Autom.Sin.38(3) (2012)375-581.
  13. J. Xu, D. Vazquez, A. M. Lopez, J. Marin, D. Ponsa, "Learning a Part-based Pedestrian Detector in Virtual World". In IEEE Transactions on Intelligent Transportation Systems (T-ITS), 2014.
  14. Navneet Dalal “Finding People in Images and Videos” PhD Thesis
  15. Datasheets and the user manuals of S3C2440.
Index Terms

Computer Science
Information Sciences

Keywords

Feature extraction friendly ARM haar cascade algorithm Pedestrian detection.

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