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D. Gavrila, J. Giebel, and S. Munder, “Vision-Based Pedestrian Detection: The PROTECTOR System”, IEEE Intelligent Vehicles Symposium, pp. 13-18, 2004. |
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| 2 |
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. |
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| 3 |
G. Monteiro, P. Peixoto, and U. Nunes, “Vision-based pedestrian detection using Haar-like features”, Robotica, pp. 16-20, 2006. |
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| 4 |
P. Dollar and Z. Tu, P. Perona, S. Belongie, “Integral channel features”, British Machine Vision Conference, pp. 1-11, 2009. |
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| 5 |
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. |
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| 6 |
P. Dollar and S. Belongie, P. Perona, “The Fastest Pedestrian Detector in the West”, British Machine Vision Conference, 2010. |
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| 7 |
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. |
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| 8 |
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. |
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| 9 |
Fast Pedestrian Detection Based on Haar Pre-Detection  |
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| 10 |
A. Broggi, M. Bertozzi, A. Fascioli, and M. Sechi, “Shape-based Pedestrian Detection”, IEEE Intelligent Vehicles Symposium 2000, pp. 215-220, 2000. |
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| 11 |
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. |
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| 12 |
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. |
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| 13 |
H. Kataoka, Y. Aoki, “Symmetrical Judgment and Improvement of CoHOG Feature Descriptor for Pedestrian Detection”, MVA2011, pp. 13-15, 2011. |
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| 14 |
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. |
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| 15 |
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. |
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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. |
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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. |
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D. Cheda, D. Ponsa, and M. Lopez, “Pedestrian candidates generation using monocular cues”, Intelligent Vehicles Symposium(IV), pp. 7-12, 2012. |
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| 19 |
A shape-independent method for pedestrian detection with far-infrared images  |
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| 20 |
Neural networks  |
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| 21 |
INRIA Person Dataset, http://pascal.inrialpes.fr/data/human/ |
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| 22 |
SVMlight, http://svmlight.joachims.org/ |
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| 23 |
CAVIAR Test Case Scenarios, http://homepages.inf.ed.ac.uk/rbf/CAVIARDATA1/ |
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