SEARCH WITHIN CONTENT
Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 9, Issue 3, Pages 1,592-1,613, DOI: https://doi.org/10.21307/ijssis-2017-931
License : (CC BY-NC-ND 4.0)
Received Date : 17-April-2016 / Accepted: 01-August-2016 / Published Online: 01-September-2016
Pedestrian detection is the key technology in Advanced Driver Assistant System (ADAS).Until recently, pedestrian detection, which is realized as the vehicle equipment, still doesn't have the mature product. So, this thesis proposes a novel pedestrian detection system on board with the E-HOG (Histogram of Gradient) IP (intellectual property), can be used as the real time vehicle equipment. Three contributions are made in this thesis. Firstly, Sobel operator cascaded Uniform Local Binary Pattern (LBP) and E-HOG is the novel structure of pedestrian detection system. The Sobel operator gives the sliding step of Uniform LBP detection window, without using the results of LBP detection window. Through this operation, the detection speed will be improved. Second, the vehicle equipment of pedestrian detection is self-developed using FPGA as core devices. Third, E-HOG IP, which is promoted based on the HOG, can extract pedestrian or other objects feature. Without sacrifice of accuracy, this pedestrian detection on board deals with 30 fps (640x480 pixels) and can be used as the real-time detection system.
 Hosang J, Benenson R, Dollar P, et al., “What makes for effective detection proposals”, IEEE Transactions on Pattern Analysis & Machine Intelligence, Vol.38, No.4, 2016, pp. 814-830.
 Benenson R, Omran M, Hosang J, et al., “Ten Years of Pedestrian Detection, What Have We Learned?” Computer Science, Vol. 8926, 2014, pp. 613-627.
Tuzel O, Porikli F, Meer P., “Pedestrian detection via classification on Riemannian manifolds”, Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 30, No.10, 2008, pp. 1713-1727.
 Park K Y, Hwang S Y., “An improved Haar-like feature for efficient object detection”, Pattern Recognition Letters, Vol. 42, No.1, 2014, pp.148-153.
 Liu Y, Yao J, Xie R, et al. Pedestrian detection from still images based on multi-feature covariances// IEEE International Conference on Information and Automation. IEEE, 2013:614-619.
Dalal N, Triggs B., “Histograms of oriented gradients for human detection,”//Computer Vision and Pattern Recognition, 2005.CVPR 2005.IEEE Computer Society Conference on. IEEE, 2005, 1: 886-893.
 Wanli O, Xingyu Z, Xiaogang W., “Single-Pedestrian Detection Aided by Two-Pedestrian Detection”, Pattern Analysis & Machine Intelligence IEEE Transactions on, vol.37, no. 4, 2015, pp.1875-1889.
 Liang F, Tang S, Wang Y, et al. “A Sparse Coding Based Transfer Learning Framework for Pedestrian Detection”, Advances in Multimedia Modeling. 2013:272-282.
 Szarvas M, Yoshizawa A, Yamamoto M, et al. “Pedestrian detection with convolutional neural networks”, Intelligent Vehicles Symposium, 2005. Proceedings. IEEE. 2005:224-229.
 Bilal M, Khan A, Khan M U K, et al. “A Low Complexity Pedestrian Detection Framework for Smart Video Surveillance Systems”, IEEE Transactions on Circuits & Systems for Video Technology, 2016:1-1.
Maji S, Berg A C, Malik J. “Classification using intersection kernel support vector machines is efficient”, Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on. IEEE, 2008: 1-8.
 Wang Y, Liu X. “Face recognition based on improved support vector clustering”, Internati-onal Journal on Smart Sensing and Intelligent Systems, vol. 7, no.4, 2014,pp.1807-1829.
 Shibayama Y, Kim H K, Fujimura K, et al., “On-board Pedestrian Detection by the Motion and the Cascaded Classifiers”, International Journal of Intelligent Transportation Systems Research, vol.9 no.3, 2011,pp.101-114.
Benenson R, Mathias M, Timofte R, et al., “Pedestrian detection at 100 fps”, Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 2012: 2903-2910.
Bauer S, Köhler S, Doll K, et al., “FPGA-GPU architecture for kernel SVM pedestrian detection”, Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on. IEEE, 2010:61-68.
 Guo Z, Zhang L, Zhang D. “Rotation invariant texture classification using LBP variance (LBPV) with global matching”, Pattern Recognition, vol. 43,no. 3, 2010, pp. 706-719.
Wang X, Han T X, Yan S. “An HOG-LBP human detector with partial occlusion handling”, Computer Vision, 2009 IEEE 12th International Conference on. IEEE, 2009: 32-39.
 Margae S E, Kerroum M A, Fakhri Y., “ Fusion of Local and Global Feature Extraction Based on Uniform LBP and DCT for Traffic Sign Recognition”, 2015, 10(1).
 Murat Alçin, İhsan Pehlivan, İsmail Koyuncu. “Hardware Design and Implementation of a Novel ANN-based Chaotic Generator in FPGA”, Optik - International Journal for Light and Electron Optics, vol. 127, no. 132016, pp. 5500-5505.
 Liu Y, Zou L, Li J, et al. “Segmentation by weighted aggregation and perceptual hash for pedestrian detection”, Journal of Visual Communication & Image Representation, vol. 36, 2016, pp. 80-89.
Lee, Chung-Hee, Kim, Dongyoung, “Improvement of processing time for stereo vision-based pedestrian detection”, Proceedings of HCI Korea. Hanbit Media, Inc., 2016.
Ouyang W, Zeng X, Wang X, “Learning Mutual Visibility Relationship for Pedestrian Detection with a Deep Model”, International Journal of Computer Vision, 2016:1-14.
Domonkos Varga, Tamás Szirányi, “Robust real-time pedestrian detection in surveillance videos”, Journal of Ambient Intelligence and Humanized Computing, 2016:1-7.
Qi B, John V, Liu Z, et al. “Pedestrian detection from thermal images: A sparse representation based approach”, Infrared Physics & Technology, vol. 76, 2016, pp.157-167.