Study on Pedestrian Detection Method Based on HOG Features and SVM

Abstract:

The research of pedestrian detection ahead of vehicle is the front direction in the field of vehicle safety assistant driving at present. The method of SVM pedestrian detection based on HOG features is studied in this paper. Firstly, the histograms of oriented gradient features between pedestrian and non-pedestrian samples are extracted. Then the features are used as an input vector of SVM algorithm, getting pedestrian classifier with a higher recognition by training. Finally the trained classifier is loaded into the online pedestrian detection system to detect the transport road image. The experimental results show that the algorithm can effectively identify the different scales and attitude pedestrian in complex background.

• Periodical: Advanced Materials Research (Volumes 268-270)
• Pages: 1786-1791
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.268-270.1786
• Online since: July 2011
• Authors: Hai Yan Xi, Zhi Tao Xiao, Fang Zhang
• Keywords: Histogram of Oriented Gradient Feature, Pedestrian Detection, Support Vector Machine (SVM)

References

[1] P. Viola, M. J. Jones, D. Snow: Detecting pedestrians using patterns of motion and appearance. The 9th ICCV, Nice, France, Vol. 1, pp.734-741, (2003).

DOI: 10.1109/iccv.2003.1238422

[2] R. Lienhart: An extended set of Haar-like features for rapid object detection. Processing of the IEEE International Conference on Image, Dec. 2002, Vol. 1, pp.900-903, (2002).

DOI: 10.1109/icip.2002.1038171

[3] N. Dalal, B. Triggs: Histograms of Oriented Gradients for Human Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 2005, San Diego, USA, Vol. II, pp.886-893, (2005).

DOI: 10.1109/cvpr.2005.177

[4] M. Enzweiler, D. Gavrila: Monocular pedestrian detection: Survey and experiments. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 31, pp.2179-2195, (2009).

DOI: 10.1109/tpami.2008.260

[5] S. Kang: Real-time Pedestrian deteetion using support vector machines. International Journal of Pattem Reeognition and Artifieial Intelligenee, Vol. 17, pp.405-416, (2003).

[6] H. Cheng, N. Zheng, J. Qin: Pedestrian Detection Using Sparse Gabor Filter and Support Vector Machine. IEEE Intelligent Vehicles Symposium, June 2005, USA, pp.583-587, (2005).

DOI: 10.1109/ivs.2005.1505166

[7] L. Yulan, M. L. Reyes, J. D. Lee: Real-Time Detection of Driver Cognitive Distraction Using Support Vector Machines. IEEE Trans. on Intelligent Transportation Systems, Vol. 8, pp.340-350, (2007).

DOI: 10.1109/tits.2007.895298

[8] G. Zhang, B. Cheng, R. Feng, J. Li: Real-time Driver Eye Detection Method Using Support Vector Machine with Hu Invariant Moments. International Conference on Machine Learning and Cybernetics, Vol. 5, pp.2999-3004, (2008).

DOI: 10.1109/icmlc.2008.4620921

[9] L. Zhao, C. Thorpe: Stereo and neural network-based pedestrian detection. IEEE Trans. on Intelligent Transportation Systems, Vol. l(3), pp.148-154, (2000).

DOI: 10.1109/6979.892151

[10] U. Franke, A. Joos: Real-time stereo vision for urban traffic scene understanding. IEEE Intelligent Vehicles Symposium, Dearborn, pp.273-278, (2000).

DOI: 10.1109/ivs.2000.898354

[11] M. Szaras, A. Yoshizawa, M. Yamamoto, J. Ogata: Pedestrian detection with convolutional neutral networks. IEEE Intelligent Vehicles Symposium, Las Vegas, Nevada, pp.224-229, (2005).

DOI: 10.1109/ivs.2005.1505106

[12] P. Viola, M. Jones: Rapid Object Detection using a Boosted Cascade of Simple Features. IEEE Computer Society on Computer Vision and Pattern Recognition, Dec. 2001, Vol. 1, pp.511-518, (2001).

DOI: 10.1109/cvpr.2001.990517