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Implementation of Robust HOG-SVM based Pedestrian Classification
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 114 - Number 19 |
| Year of Publication: 2015 |
| Authors: Reecha P. Yadav, Vinuchackravarthy Senthamilarasu, Krishnan Kutty, Sunita P. Ugale |
10.5120/20084-2026
|
Reecha P. Yadav, Vinuchackravarthy Senthamilarasu, Krishnan Kutty, Sunita P. Ugale . Implementation of Robust HOG-SVM based Pedestrian Classification. International Journal of Computer Applications. 114, 19 ( March 2015), 10-16. DOI=10.5120/20084-2026
Abstract
Achieving pedestrian protection by means of computer vision is not a new topic in the field of computer vision research; however it is still being pursued with renewed interest because of the huge scope for performance improvement in the existing systems. Generally, the task of pedestrian detection (PD) involves stages such as pre-processing, ROI selection, feature extraction, classification, verification/refinement and tracking. Of all the steps involved in the PD framework, the paper presents the work done towards implementing the feature extraction and classification stages in particular. It is of paramount importance that the extracted features from the image should be robust and distinct enough to help the classifier distinguish between a pedestrian and a non-pedestrian, while a good classification algorithm would go a long way in precisely identifying a pedestrian as well as in simplifying the verification stage of the PD framework. The presented work focuses on the implementation of the Histogram of Oriented Gradients (HOG) features with modified parameters that can represent accurate intrinsic information of the image. Classification is achieved using Support Vector Machine (SVM). However instead of employing a readily available SVM library, the linear SVM implemented uses the Sequential Minimal Optimization (SMO) algorithm. The results observed by this HOG-SVM combination show promise to be the best feature extraction cum classification module for a full-fledged PD system.
References
- World Health Organization (WHO). 2013. Global Status Report on Road Safety 2013: Supporting a Decade of Action.
- Gero´nimo D. , Lo´pez A. M. , Sappa A. D. , and Graf T. , "Survey of Pedestrian Detection for Advanced Driver Assistance Systems", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010 .
- Dalal N. and Triggs B. 2005. Histograms of Oriented Gradients for Human Detection. In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition.
- Platt J. 1999. Fast training of support vector machines using sequential minimal optimization. Advances in kernel methods—support vector learning 3.
- Yadav R. P. , Senthamilarasu V. , Kutty K. , Vaidya V. , Ugale S. P. 2015 (in press). A Review on Day-Time Pedestrian Detection. SAE 2015 World Congress.
- Hussain S. and Triggs B. 2010. Feature sets and dimensionality reduction for visual object detection. In British Machine Vision Conference.
- Liu Y. , Yao J. , Xie R. , and Zhu S. 2013. Pedestrian Detection from Still Images Based on Multi-Feature Covariances. In Proceeding of the IEEE International Conference on Information and Automation.
- Jun B. , Choi I. , and Kim D, "Local Transform Features and Hybridization for Accurate Face and Human Detection", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013.
- Enzweiler M. and Gavrila D. M. , "A Multilevel Mixture-of-Experts Framework for Pedestrian Classification", IEEE Transactions on Image Processing, 2011.
- Li B. , Li Y. , Tian B. , Zhu F. H. , Xiong G. , Wang K. 2013. Part-based pedestrian detection using grammar model and ABM-HOG features. IEEE International Conference on Vehicular Electronics and Safety.
- Wojek C. and Schiele B. 2008. A Performance Evaluation of Single and Multi-feature People Detection. G. Rigoll (Ed. ): DAGM Symposium.
- Wu B. and Nevatia R. 2007. Simultaneous Object Detection and Segmentation by Boosting Local Shape Feature Based Classifier. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.
- Sabzmeydani P. and Mori G. 2007. Detecting Pedestrians by Learning Shapelet Features. In Proceedings of IEEE Conference Computer Vision and Pattern Recognition.
- Dalal N. , Triggs B. , and Schmid C. 2006. Human Detection Using Oriented Histograms of Flow and Appearance. In Proceedings of European Conference on Computer Vision.
- Munder S. and Gavrila D. M. , "An Experimental Study on Pedestrian Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006.
- Felzenszwalb P. , Girshick R. , McAllester D. and Ramanan D. , "Object Detection with Discriminatively Trained Part Based Models", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010.
- Tang Y. , Liu W. , Jianwei W. 2010. An Improved Pedestrian Detection Algorithm Based on AdaBoost Cascading Stucture. In Proceedings of the 8th World Congress on Intelligent Control and Automation.
- Cerri P. , Gatti L. , Mazzei L. , Pigoni F. et al. 2010. Day and Night Pedestrian Detection Using Cascade AdaBoost System. In IEEE Annual Conference on Intelligent Transportation Systems.
- XuY. W. , Cao X. B. , Qiao H. , and Wang F. Y. 2006. A cascaded classifier for pedestrian detection. In Proceedings of IEEE Intelligent Vehicle Symposium.
- Ouyang, W. , Wang, X. 2013. Joint deep learning for pedestrian detection. In ICCV.
- Tong W. , Hong H. , Fang H. , Xie Q. , et al. 2003. Decision Forest: Combining the Predictions of Multiple Independent Decision Tree Models. Published by the American Chemical Society.
- Benenson R. , Omran M. , Hosang J. , Schiele B. 2014. Ten years of pedestrian detection, what have we learned? ECCV 2014.
- Mak G. 2000. The Implementation Of Support Vector Machines Using The Sequential Minimal Optimization Algorithm. Master's Thesis, McGill University.
- Gerónimo D. , Sappa A. D. , Ponsa D. and López A. M. 2010. 2D-3D Based on-board pedestrian detection system. In special issue on Intelligent Vision Systems in Computer Vision and Image Understanding.
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