Browse > Article
http://dx.doi.org/10.5573/ieek.2013.50.10.162

An Efficient Pedestrian Recognition Method based on PCA Reconstruction and HOG Feature Descriptor  

Kim, Cheol-Mun (PLK Technologies)
Baek, Yeul-Min (Hyundai MOBIS)
Kim, Whoi-Yul (Department of Electronics Computer Engineering, Hanyang University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.50, no.10, 2013 , pp. 162-170 More about this Journal
Abstract
In recent years, the interests and needs of the Pedestrian Protection System (PPS), which is mounted on the vehicle for the purpose of traffic safety improvement is increasing. In this paper, we propose a pedestrian candidate window extraction and unit cell histogram based HOG descriptor calculation methods. At pedestrian detection candidate windows extraction stage, the bright ratio of pedestrian and its circumference region, vertical edge projection, edge factor, and PCA reconstruction image are used. Dalal's HOG requires pixel based histogram calculation by Gaussian weights and trilinear interpolation on overlapping blocks, But our method performs Gaussian down-weight and computes histogram on a per-cell basis, and then the histogram is combined with the adjacent cell, so our method can be calculated faster than Dalal's method. Our PCA reconstruction error based pedestrian detection candidate window extraction method efficiently classifies background based on the difference between pedestrian's head and shoulder area. The proposed method improves detection speed compared to the conventional HOG just using image without any prior information from camera calibration or depth map obtained from stereo cameras.
Keywords
Histogram of oriented gradients;
Citations & Related Records
연도 인용수 순위
  • Reference
1 G. Xu, X. Wu, L. Liu, and Z. Wu, "Real-time Pedestrian Detection Based on Edge Factor and Histogram of Oriented Gradient", International Conference on Information and Automation (ICIA), pp. 384-389, 2011.
2 W. Xing, Y. Zhao, R. Cheng, J. Xu, S. Lv, X. Wang, "Fast Pedestrian Detection Based on Haar Pre-Detection", International Journal of Computer and Communication Engineering, Vol. 1, pp. 207-209, 2012.
3 A. Broggi, M. Bertozzi, A. Fascioli, and M. Sechi, "Shape-based Pedestrian Detection", IEEE Intelligent Vehicles Symposium 2000, pp. 215-220, 2000.
4 T. Kancharla, P. Kharade, S. Gindi, K. Kutty, and V. Vaidya, "Edge based Segmentation for Pedestrian Detection using NIR Camera", 2011 International Conference on Image Information Processing, pp. 1-6, 2011.
5 A. Mazoul, K. Zebbara, and M. Ansari, " Street crossing pedestrian detection based on edge curves motion", International Journal of Computer Applications, vol. 41, pp. 570-575, 2007.
6 H. Kataoka, Y. Aoki, "Symmetrical Judgment and Improvement of CoHOG Feature Descriptor for Pedestrian Detection", MVA2011, pp. 13-15, 2011.
7 Sang-Hun Kim, Dong-Gon Yoo, and Young- Hwan Kim, "High Performance Pedestrian Detection System Using A Cascade Algorithm Structure", IEEK SoC Conference, pp. 91-94, 2011.
8 A. Cosma, R. Brehar, and S. Nedevschi, "Part-based pedestrian detection using HoG features and vertical symmetry", Intelligent Computer Communication and Processing (ICCP), pp. 229-236, 2012.
9 G. Lie, W. Rongben, J. Lisheng, and Z. Mingheng, "STUDY ON PEDESTRIAN DETECTION AHEAD OF VEHICLE BASED ON MACHINE VISION", International Conference on Transportation Engineering ASCE, pp. 570-575, 2007.
10 G. Lie, W. Rongben, J. Lisheng, L. Linhui, and Y. Lu, "Algorithm Study for Pedestrian Detection Based on Monocular Vision", Vehicular Electronics and Safety, pp. 83-87, 2006.
11 D. Cheda, D. Ponsa, and M. Lopez, "Pedestrian candidates generation using monocular cues", Intelligent Vehicles Symposium(IV), pp. 7-12, 2012.
12 Y. Fang, K. Yamada, Y. Ninomiya, K. Horn, and I. Masaki, "A Shape-independent method for pedestrian detection with far-infrared images", Vehicular Technology, pp. 1679-1697, 2004.
13 Guilherme V. Carvalho, Lailson B. Moraes, "A weighted image reconstruction based on PCA for pedestrian Detection", Neural Networks (IJCNN), The 2011 International Joint Conference on, pp. 2005-2011, 2011.
14 INRIA Person Dataset, http://pascal.inrialpes.fr/data/human/
15 SVMlight, http://svmlight.joachims.org/
16 CAVIAR Test Case Scenarios, http://homepages.inf.ed.ac.uk/rbf/CAVIARDATA1/
17 D. Gavrila, J. Giebel, and S. Munder, "Vision-Based Pedestrian Detection: The PROTECTOR System", IEEE Intelligent Vehicles Symposium, pp. 13-18, 2004.
18 P. Viola, M. Jones, and D. Snow, "Detecting Pedestrians Using Patterns of Motion and Appearance", IEEE International Conference on Computer Vision, pp. 153-161, 2005.
19 G. Monteiro, P. Peixoto, and U. Nunes, "Vision-based pedestrian detection using Haar-like features", Robotica, pp. 16-20, 2006.
20 P. Dollar and Z. Tu, P. Perona, S. Belongie, "Integral channel features", British Machine Vision Conference, pp. 1-11, 2009.
21 Dalal, N and Triggs, B, "Histograms of oriented gradients for human detection". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 886-893, 2005.
22 P. Dollar and S. Belongie, P. Perona, "The Fastest Pedestrian Detector in the West", British Machine Vision Conference, 2010.
23 R. Benenson and M. Mathias, R. Timofte, L. Van Gool, "Pedestrian detection at 100 frames per second". Computer Vision and Pattern Recognition (CVPR), pp. 2903-2910, 2012.