Intelligent Transport System for Human Detection with an Efficient HOG Extraction Method
ELAVARASI. K PG Scholar , Dr. R. KALPANA, Professor
A robust human detection system in an intelligent transportation system is desired by people and becomes essential to industries such as surveillance, automotive systems, and robotics. However, there are still many encounters to attain ideal human detection, such as the diversity of object appearance and the interference of an image due to light changing. These challenges make human detection a more challenging and unreliable task. Histograms of Oriented Gradients (HOG) are proven to be able to knowingly outpace existing feature sets for human detection. In this work, motivation only on the feature extraction method using HOG for real-time applications. For simplicity, a linear support vector machine (SVM) is used as a baseline classifier throughout the study. It is obvious that the calculation of HOG feature extraction is computationally complicated and unsuitable for hardware implementation. Hence, to adopt some approximate techniques, it will reduce implementation complexity and to improve extraction speed.
Human Detection; Feature extraction; Histograms of Oriented Gradients (HOG); Complexity; baseline classifier; object appearance.
 Pei-Yin Chen, Chien-Chuan Huang, Chih-Yuan Lien, and Yu-Hsien Tsai, “An Efficient Hardware implementation of HOG Feature Extraction for Human Detection”, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 15, NO. 2, APRIL 2014.
 S. J. Krotosky and M. M. Trivedi, “Person surveillance using visual and infrared imagery,” IEEE Trans. Circuits Syst. Video Technol., vol. 18, no. 8, pp. 1096–1105, Oct. 2008.
 C. Papageorgiou and T. Poggio, “A trainable system for object detection,”Int. J. Comput. Vision, vol. 38, no. 1, pp. 15–33, Jun. 2000.
 N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proc. IEEE Conf. Comput. Vision Pattern Recog., Jun. 2005,vol. 1, pp. 886–893.  K. Mizuno, Y. Terachi, and K. Takagi, “Architectural study of HOG feature extraction processor for real-time object detection,” in Proc. IEEE Workshop Signal Process. Syst., Oct. 2012, pp. 197–202.
 Y. Pang, Y. Yuan, X. Li, and J. Pan, “Efficient HOG human detection,” Signal Process., vol. 91, no. 4, pp. 773–781, Apr. 2011.
 T. P. Cao and G. Deng, “Real-time vision-based stop sign detection system on FPGA,” in Proc. Digital Image Comput., Tech. Appl. Los Alamitos, Canberra, ACT, Australia, 2008, pp. 465–471, IEEE Computer Society.
 S. Bauer, U. Brunsmann, and S. S. -Macht, “FPGA implementation of a HOG-based pedestrian recognition system,” in MPC-Workshop, Jul. 2009, pp. 49–58.
 M. Hiromoto and R.Miyamoto, “Hardware architecture for high-accuracy real-time pedestrian detection with CoHOG features,” in Proc. IEEEICCVW, 2009, pp. 894–899.
 R. Kadota, H. Sugano, M. Hiromoto, H. Ochi, R. Miyamoto, and Y. Nakamura, “Hardware architecture for HOG feature extraction,” in Proc. IEEE Conf. Intell. Inf. Hiding Multimedia Signal Process., Nov. 2009, pp. 1330–1333.
[ELAVARASI. K PG Scholar , Dr. R. KALPANA, Professor (2015) Intelligent Transport System for Human Detection with an Efficient HOG Extraction Method IJIRCST Vol-3 Issue-3 Page No-33-36] (ISSN 2347 - 5552). www.ijircst.org
ELAVARASI. K PG Scholar
Department Of Computer Science & Engineering, IFET College of Engineering. Tamilnadu, India