Call for Paper

CAE solicits original research papers for the July 2023 Edition. Last date of manuscript submission is June 30, 2023.

Read More

Pedestrian Detection Technique’s – A Review

Vrushali B. Ghule, S.S. Katariya. Published in Information Sciences.

Communications on Applied Electronics
Year of Publication: 2015
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Vrushali B. Ghule, S.S. Katariya

Vrushali B Ghule and S S Katariya. Article: Pedestrian Detection Technique’s – A Review. Communications on Applied Electronics 3(6):10-12, December 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Vrushali B. Ghule and S.S. Katariya},
	title = {Article: Pedestrian Detection Technique’s – A Review},
	journal = {Communications on Applied Electronics},
	year = {2015},
	volume = {3},
	number = {6},
	pages = {10-12},
	month = {December},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


Pedestrian is a main part of the road system.To detect pedestrian is a critical thing in a computer vision .There are many methods are available to detect pedestrian and subsequently to take some action.In this review based paper we have discussed some popular techniques.


  1. D. Geronimo, A.M. Lopez, A.D. Sappa, and T. Graf, “Survey on Pedestrian Detection for Advanced Driver Assistance Systems,”IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 7, pp. 1239-1258, July 2010
  2. M. Enzweiler and D.M. Gavrila, “Monocular PedestrianDetection: Survey and Experiments,” IEEE Trans. PatternAnalysis and Machine Intelligence, vol. 31, no. 12, pp. 2179-2195, Dec. 2009.
  3. N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2005.
  4. D. Martin, C. Fowlkes, and J. Malik, “Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 5, pp. 530-549, May 2004.
  5. D. Scharstein and R. Szeliski, “A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms,” Int’l J. Computer Vision, vol. 47, pp. 7-42, 2002.
  6. E. Seemann, M. Fritz, and B. Schiele, “Towards Robust Pedestrian Detection in Crowded Image Sequences,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007
  7. M. Hussein, F. Porikli, and L. Davis, “A ComprehensiveEvaluation Framework and a Comparative Study for HumanDetectors,” IEEE Trans. Intelligent Transportation Systems, vol. 10,no. 3, pp. 417-427, Sept. 2009.
  8. T. Ojala, M. Pietik¨ainen, and D. Harwood. A comparativestudy of texture measures with classification based on featured distributions. Pattern Recognition, 29(1):51–59, 1996
  9. C.H. Lampert, M.B. Blaschko, and T. Hofmann, “Beyond Sliding Windows: Object Localization by Effcient subwindow Search,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
  10. T. Moeslund, A. Hilton, and V. Kru¨ ger, “A Survey of Advances in Vision-Based Human Motion Capture and Analysis,” Computer Vision and Image Understanding, vol. 104, nos. 2/3, pp. 90-126, 2006
  11. T. Gandhi and M.M. Trived, “Pedestrian Protection Systems:Issues, Survey, and Challenges,” IEEE Trans. Intelligent Transportation Systems, vol. 8, no. 3, pp. 413-430, Sept. 2007.
  12. M. Bertozzi, A. Broggi, R. Chapuis, F. Chausse, A. Fascioli, and A.Tibaldi, “Shape-Based Pedestrian Detection and Localization,”Proc. IEEE Int’l Conf. Intelligent Transportation Systems, pp. 328-333,2003.
  13. Wentao Yao, Zhidong Deng” A Robust Pedestrian Detection Approach Based on Shapelet Feature and Haar Detector Ensembles” Tsinghua Science and Technology Volume 17, Number 1, February 2012 llpp40-50
  14. Pawan Sinha,Tomaso A.Poggio “Pedestrian detection using wavelet templates”Processing /CVRR,iee computer society conference on computer vision and pattern recognition.-1977
  15. Shuoping Wang, Zhike Han, Li Zhu and Qi Chen,” A Novel Approach to Design the Fast Pedestrian Detection for Video Surveillance System” International Journal of Security and Its Applications Vol.8, No.1 (2014), pp.93-102
  16. K. Fukushima, S. Miyake, and T. Ito, “Neocognitron: A Neural Network Model for a Mechanism of Visual Pattern Recognition,”IEEE Trans. Systems, Man, and Cybernetics, vol. 13, pp. 826-834,1983
  17. 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


Pedestrian Detection System, Tracking of Pedestrian, Reaction of system.