Research Article
Street crossing pedestrian detection based on edge curves motion
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 41 - Issue 16 |
| Published: March 2012 |
| Authors: Abdenbi Mazoul, Khalid Zebbara, Mohamed El Ansari |
10.5120/5624-7927
|
Abdenbi Mazoul, Khalid Zebbara, Mohamed El Ansari . Street crossing pedestrian detection based on edge curves motion. International Journal of Computer Applications. 41, 16 (March 2012), 20-24. DOI=10.5120/5624-7927
@article{ 10.5120/5624-7927,
author = { Abdenbi Mazoul,Khalid Zebbara,Mohamed El Ansari },
title = { Street crossing pedestrian detection based on edge curves motion },
journal = { International Journal of Computer Applications },
year = { 2012 },
volume = { 41 },
number = { 16 },
pages = { 20-24 },
doi = { 10.5120/5624-7927 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2012
%A Abdenbi Mazoul
%A Khalid Zebbara
%A Mohamed El Ansari
%T Street crossing pedestrian detection based on edge curves motion%T
%J International Journal of Computer Applications
%V 41
%N 16
%P 20-24
%R 10.5120/5624-7927
%I Foundation of Computer Science (FCS), NY, USA
Abstract
This paper presents a real-time method for detecting pedestrians using vertical motion form two consecutives frames. We used association approach to match edge curves between consecutive images. Significant motions can be found using horizontal-vertical projection histogram. Then the pedestrian detection process is achieved in two steps. The first one searches the region of interest by using the intersection of vertical and horizontal projection of significant motion. The second step applies the Adaboost classifier on the region of interest provided by the first step. The proposed approach has been tested on different city traffic image sequences acquired by a camera mounted in a moving car. The results demonstrate the effectiveness of the proposed method.
References
- Andreas Geiger and Martin Roser and Raquel Urtasun, "Efficient Large-Scale Stereo Matching", Asian Conference on Computer Vision,2010 November, Queenstown, New Zealand.
- B. Wu and R. Nevatia. "Cluster boosted tree classifier for multi-view, multi-pose object detection". In ICCV, 2007.
- Bernd Kitt and Andreas Geiger and Henning Lategahn, "Visual Odometry based on Stereo Image Sequences with RANSAC-based Outlier Rejection Scheme", IEEE Intelligent Vehicles Symposium, 2010 June, San Diego, USA.
- Broggi, A. F, "stereo-based preprocessing for human shape localization in unstructured environments" In Proc. of the IEEE Intelligent Vehicle Symposium. , pp. 410-415, Ohio USA, 2003.
- C. J. C. Burges. "A tutorial on support vector machines for pattern recognition". Data Mining and Knowledge Discovery, 2:121–167, 1998.
- D. M. Gavrila and S. Munder. "Multi-cue pedestrian detection andtracking from a moving vehicle". IJCV, pages 41–59, 2007.
- E. Seemann, M. Fritz, and B. Schiele. "Towards robust pedestrian detection in crowded image sequences". In CVPR, 2007.
- Gavrila, D. , "Sensor-based pedestrian protection", IEEE Intelligent Systems, pp. 77-81, 2001.
- J. Canny, "A computational approach to edge detection", IEEE Trans. Pattern Anal. Mach. Intell. , vol. PAMI-8, no. 6, pp. 679–698, Nov. 1986.
- Kurita, K. N. "Boosting Soft-Margin SVM with Feature Selection for Pedestrian Detection", Springer-Verlag Berlin Heidelberg , pp. 22-31, 2005.
- M. Bertozzi, "Infrared Stereo Vision-based Pedestrian DetecIntelligent Vehicles Symposium", 2005, pp. 23-28. Parma:IEEE.
- M. El Ansari, Abdenbi Mazoul, Abdelaziz Bensrhair, George Bebis, "A Real-time Spatio-Temporal Stereo Matching for Road Applications", 2011 14th International IEEE Conference on Intelligent Transportation Systems Washington, DC, USA. October 5-7, 2011.
- M. El-Ansari, S. Mousset, A. Bensrhair, and G. Bebis, "Temporal consistent fast stereo matching for adavnced driver assistance systems (ADAS) ", in Proc. of IEEE Intelligent Vehciles Symposium, San Diego, CA, USA, June 21-24 2010, pp. 825–831.
- M. El-Ansari, S. Mousset, and A. Bensrhair, "Temporal consistent real-time stereo for intelligent vehicles", Pattern Recognition Letters, vol. 31, no. 11, pp. 1226–1238, August 2010.
- Munder, D. M, "An Experimental Study on Pedestrian Classification", IEEE Transactions on Pattern Analysis and Machine Intelligence. 2006,pp. 1863-1868.
- P. Sabzmeydani and G. Mori. "Detecting pedestrians by learning shapelet features". In CVPR, 2007.
- P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features", in CVPR, 2001.
- P. Viola and M. Jones. "Robust real-time object detection". IJCV, 57(2):137–154, 2004.
- P. Viola, M. J. , "Stereo- and Neural Network-Based Pedestrian Detection", IEEE Trans. Intelligent Transportation Systems, vol 1, no. 3, 2000.
- P. Viola, M. Jones, and D. Snow. "Detecting pedestrians using patterns of motion and appearance". In CVPR, 2003.
- R. E. Schapire, "The boosting approach to machine learning: An overview", In MSRI Workshop on Nonlinear Estimation and Classification, 2002, 2002.
- S. Munder, C. Schn¨orr, and D. Gavrila. "Pedestrian detection and tracking using a mixture of view-based shape-texture models". In IEEE Transactions on Intelligent Transportation Systems, 2008.
- Thorpe, L. ,"Stereo- and Neural Network-Based Pedestrian Detection", IEEE Trans. Intelligent Transportation Systems, vol 1, no. 3,2000.
- Zebbara Khalid , Abdenbi Mazoul, Mohamed El Ansari "A new vehicle detection method", International Journal of Advanced Computer Science and Applications (IJACSA), Special Issue on Artificial Intelligence, Volume 2 No. 8 August 2011.
- Zebbara Khalid , Abdenbi Mazoul, Mohamed El Ansari, "On Road Vehicle Detection using Association Approach", International Journal of Computer Applications (IJCA) (0975 – 8887) Volume 34– No. 2, November 2011.
Index Terms
Keywords