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Issue No.07 - July (2010 vol.32)
pp: 1239-1258
David Gerónimo , Universitat Autònoma de Barcelona, Barcelona
Antonio M. López , Universitat Autònoma de Barcelona, Barcelona
Angel D. Sappa , Universitat Autònoma de Barcelona, Barcelona
Thorsten Graf , Volkswagen AG, Wolsburg
ABSTRACT
Advanced driver assistance systems (ADASs), and particularly pedestrian protection systems (PPSs), have become an active research area aimed at improving traffic safety. The major challenge of PPSs is the development of reliable on-board pedestrian detection systems. Due to the varying appearance of pedestrians (e.g., different clothes, changing size, aspect ratio, and dynamic shape) and the unstructured environment, it is very difficult to cope with the demanded robustness of this kind of system. Two problems arising in this research area are the lack of public benchmarks and the difficulty in reproducing many of the proposed methods, which makes it difficult to compare the approaches. As a result, surveying the literature by enumerating the proposals one--after-another is not the most useful way to provide a comparative point of view. Accordingly, we present a more convenient strategy to survey the different approaches. We divide the problem of detecting pedestrians from images into different processing steps, each with attached responsibilities. Then, the different proposed methods are analyzed and classified with respect to each processing stage, favoring a comparative viewpoint. Finally, discussion of the important topics is presented, putting special emphasis on the future needs and challenges.
INDEX TERMS
ADAS, pedestrian detection, on-board vision, survey.
CITATION
David Gerónimo, Antonio M. López, Angel D. Sappa, Thorsten Graf, "Survey of Pedestrian Detection for Advanced Driver Assistance Systems", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 7, pp. 1239-1258, July 2010, doi:10.1109/TPAMI.2009.122
REFERENCES
[1] D. Gavrila, P. Marchal, and M.-M. Meinecke, "SAVE-U, Deliverable 1-A: Vulnerable Road User Scenario Analysis," technical report, Information Soc. Technology Programme of the EU, 2003.
[2] W. Jones, "Building Safer Cars," IEEE Spectrum, vol. 39, no. 1, pp. 82-85, Jan. 2002.
[3] United Nations—Economic Commission for Europe "Statistics of Road Traffic Accidents in Europe and North America," 2005.
[4] R. Bishop, Intelligent Vehicle Technologies and Trends. Artech House, Inc., 2005.
[5] L. Vlacic, M. Parent, and F. Harashima, Intelligent Vehicle Technologies. Butterworth-Heinemann, 2001.
[6] T. Heinrich, "Bewertung Von Technischen Maßnahmen zum Fußgängerschutz am Kraftfahrzeug," technical report, Technische Univ. Berlin, 2003.
[7] T. Moeslund, A. Hilton, and V. Krüger, "A Survey of Advances in Vision-Based Human Motion Capture and Analysis," Computer Vision and Image Understanding, vol. 104, nos. 2/3, pp. 90-126, 2006.
[8] D. Forsyth, O. Arikan, L. Ikemoto, J. O'Brien, and D. Ramanan, Computational Studies of Human Motion: Part 1, Tracking and Motion Synthesis. Now publishers, 2005.
[9] D. Gavrila, "Sensor-Based Pedestrian Protection," IEEE Intelligent Systems, vol. 16, no. 6, pp. 77-81, Nov./Dec. 2001.
[10] T. Gandhi and M.M. Trivedi, "Pedestrian Collision Avoidance Systems: A Survey of Computer Vision Based Recent Studies," Proc. IEEE Int'l Conf. Intelligent Transportation Systems, pp. 976-981, 2006.
[11] T. Gandhi and M.M. Trived, "Pedestrian Protection Systems: Issues, Survey, and Challenges," IEEE Trans. Intelligent Transportation Systems, vol. 8, no. 3, pp. 413-430, Sept. 2007.
[12] M.-H. Yang, D.J. Kriegman, and N. Ahuja, "Detecting Faces in Images: A Survey," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 1, pp. 34-58, Jan. 2002.
[13] Z. Sun, G. Bebis, and R. Miller, "On-Road Vehicle Detection: A Review," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 5, pp. 694-711, May 2006.
[14] D. Gerónimo, A. López, and A. Sappa, "Computer Vision Approaches for Pedestrian Detection: Visible Spectrum Survey," Proc. Third Iberian Conf. Pattern Recognition and Image Analysis, pp. 547-554, 2007.
[15] S. Nayar and V. Branzoi, "Adaptive Dynamic Range Imaging: Optical Control of Pixel Exposures over Space and Time," Proc. Int'l Conf. Computer Vision, vol. 2, pp. 1168-1175, 2003.
[16] S. Marsi, G. Impoco, A. Ukovich, S. Carrato, and G. Ramponi, "Video Enhancement and Dynamic Range Control of HDR Sequences for Automotive Applications," EURASIP J. Advances in Signal Processing, vol. 2007, p. 9, 2007.
[17] P. Knoll, "HDR Vision for Driver Assistance," High-Dynamic-Range (HDR) Vision, B. Hoefflinger, ed., pp. 123-136, Springer, 2007.
[18] A. Broggi, M. Bertozzi, and A. Fascioli, "Self-Calibration of a Stereo Vision System for Automotive Applications," Proc. IEEE Int'l Conf. Robotics and Automation, pp. 3698-3703, 2001.
[19] T. Dang and C. Hoffmann, "Stereo Calibration in Vehicles," Proc. IEEE Intelligent Vehicles Symp., pp. 268-273, 2004.
[20] M. Bertozzi, A. Broggi, M. Carletti, A. Fascioli, T. Graf, P. Grisleri, and M.-M. Meinecke, "IR Pedestrian Detection for Advanced Driver Assistance Systems," Proc. Pattern Recognition Symp., pp. 582-590, 2003.
[21] A. Broggi, P. Grisleri, T. Graf, and M.-M. Meinecke, "A Software Video Stabilization System for Automotive Oriented Applications," Proc. Vehicular Technology Conf., vol. 5, pp. 2760-2764, 2005.
[22] L. Bombini, P. Cerri, P. Grisleri, S. Scaffardi, and P. Zani, "An Evaluation of Monocular Image Stabilization Algorithms for Automotive Applications," Proc. IEEE Int'l Conf. Intelligent Transportation Systems, pp. 1562-1567, 2006.
[23] D. Hoiem, A. Efros, and M. Hebert, "Putting Objects in Perspective," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 2137-2144, 2006.
[24] R. Labayrade, D. Aubert, and J. Tarel, "Real Time Obstacle Detection in Stereovision on Non Flat Road Geometry through 'v-Disparity' Representation," Proc. IEEE Intelligent Vehicles Symp., vol. 2, pp. 17-21, 2002.
[25] Z. Hu and K. Uchimura, "U-V-Disparity: An Efficient Algorithm for Stereovision Based Scene Analysis," Proc. IEEE Intelligent Vehicles Symp., pp. 48-54, 2005.
[26] A. Sappa, F. Dornaika, D. Ponsa, D. Gerónimo, and A. López, "An Efficient Approach to Onboard Stereo Vision System Pose Estimation," IEEE Trans. Intelligent Transportation Systems, vol. 9, no. 3, pp. 476-490, Sept. 2008.
[27] S. Nedevschi, R. Danescu, D. Frentiu, T. Marita, F. Oniga, C. Pocol, T. Graf, and R. Schmidt, "High Accuracy Stereovision Approach for Obstacle Detection on Non-Planar Roads," Proc. IEEE Intelligent Eng. Systems, pp. 211-216, 2004.
[28] A. Ess, B. Leibe, and L. VanGool, "Depth and Appearance for Mobile Scene Analysis," Proc. Int'l Conf. Computer Vision, 2007.
[29] A. Ess, B. Leibe, K. Schindler, and L. VanGool, "A Mobile Vision System for Robust Multi-Person Tracking," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[30] D. Gerónimo, A. Sappa, A. López, and D. Ponsa, "Adaptive Image Sampling and Windows Classification for On-Board Pedestrian Detection," Proc. Fifth Int'l Conf. Computer Vision Systems, 2007.
[31] N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 886-893, 2005.
[32] C. Papageorgiou and T. Poggio, "A Trainable System for Object Detection," Int'l J. Computer Vision, vol. 38, no. 1, pp. 15-33, 2000.
[33] Q. Zhu, S. Avidan, M.-C. Yeh, and K.-T. Cheng, "Fast Human Detection Using a Cascade of Histrograms of Oriented Gradients," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 1491-1498, 2006.
[34] C. Wojek, G. Dorkó, A. Schulz, and B. Schiele, "Sliding-Windows for Rapid Object Class Localization: A Parallel Technique," Proc. Symp. German Assoc. for Pattern Recognition, pp. 71-81, 2008.
[35] W. Zhang, G. Zelinsky, and D. Samaras, "Real-Time Accurate Object Detection Using Multiple Resolutions," Proc. Int'l Conf. Computer Vision, pp. 1-8, 2007.
[36] F. Miau, C. Papageorgiou, and L. Itti, "Neuromorphic Algorithms for Computer Vision and Attention," Proc. Int'l Symp. Optical Science and Technology, pp. 12-23, 2001.
[37] L. Itti, C. Koch, and E. Niebur, "A Model of Saliency-Based Visual Attention for Rapid Scene Analysis," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 11, pp. 1254-1259, Nov. 1998.
[38] A. Broggi, A. Fascioli, I. Fedriga, A. Tibaldi, and M.D. Rose, "Stereo-Based Preprocessing for Human Shape Localization in Unstructured Environments," Proc. IEEE Intelligent Vehicles Symp., pp. 410-415, 2003.
[39] A. Broggi, M. Bertozzi, A. Fascioli, and M. Sechi, "Shape-Based Pedestrian Detection," Proc. IEEE Intelligent Vehicles Symp., pp. 215-220, 2000.
[40] M. Bertozzi, A. Broggi, R. Chapuis, F. Chausse, A. Fascioli, and A. Tibaldi, "Shape-Based Pedestrian Detection and Localization," Proc. IEEE Int'l Conf. Intelligent Transportation Systems, pp. 328-333, 2003.
[41] M. Bertozzi, A. Broggi, A. Fascioli, A. Tibaldi, R. Chapuis, and F. Chausse, "Pedestrian Localization and Tracking System with Kalman Filtering," Proc. IEEE Intelligent Vehicles Symp., pp. 584-589, 2004.
[42] M. Bertozzi, A. Broggi, A. Fascioli, T. Graf, and M.-M. Meinecke, "Pedestrian Detection for Driver Assistance Using Multiresolution Infrared Vision," IEEE Trans. Vehicular Technology, vol. 53, no. 6, pp. 1666-1678, Nov. 2004.
[43] F. Suard, A. Rakotomamonjy, A. Bensrhair, and A. Broggi, "Pedestrian Detection Using Infrared Images and Histograms of Oriented Gradients," Proc. IEEE Intelligent Vehicles Symp., pp. 206-212, 2006.
[44] A. Broggi, R. Fedriga, A. Tagliati, T. Graf, and M.-M. Meinecke, "Pedestrian Detection on a Moving Vehicle: An Investigation about Near Infra-Red Images," Proc. IEEE Intelligent Vehicles Symp., pp. 431-436, 2006.
[45] T. Tsuji, H. Hattori, M. Watanabe, and N. Nagaoka, "Development of Night-Vision System," IEEE Trans. Intelligent Transportation Systems, vol. 3, no. 3, pp. 203-209, Sept. 2002.
[46] Q. Tian, H. Sun, Y. Luo, and D. Hu, "Nighttime Pedestrian Detection with a Normal Camera Using SVM Classifier," Proc. Int'l Symp. Neural Networks, pp. 189-194, 2005.
[47] Y. Fang, K. Yamada, Y. Ninomiya, B. Horn, and I. Masaki, "A Shape-Independent Method for Pedestrian Detection with Far-Infrared Images," IEEE Trans. Vehicular Technology, vol. 53, no. 6, pp. 1679-1697, Nov. 2004.
[48] M. Bertozzi, A. Broggi, A. Lasagni, and M.D. Rose, "Infrared Stereo Vision-Based Pedestrian Detection," Proc. IEEE Intelligent Vehicles Symp., pp. 24-29, 2005.
[49] M. Oberländer, "Hypermutation Networks—A Discrete Approach to Machine Perception," Proc. Third Weightless Neural Networks Workshop, 2005.
[50] U. Meis, M. Oberländer, and W. Ritter, "Reinforcing the Reliability of Pedestrian Detection in Far-Infrared Sensing," Proc. IEEE Intelligent Vehicles Symp., pp. 779-783, 2004.
[51] M. Mählisch, M. Oberländer, O. Löhlein, D. Gavrila, and W. Ritter, "A Multiple Detector Approach to Low-Resolution FIR Pedestrian Recognition," Proc. IEEE Intelligent Vehicles Symp., pp. 325-330, 2005.
[52] U. Franke and I. Kutzbach, "Fast Stereo Based Object Detection for Stop & Go Traffic," Proc. IEEE Intelligent Vehicles Symp., pp. 339-344, 1996.
[53] U. Franke, D. Gavrila, S. Görzig, F. Lindner, F. Paetzold, and C. Wöhler, "Autonomous Driving Goes Downtown," IEEE Intelligent Systems, vol. 13, no. 6, pp. 40-48, Nov./Dec. 1999.
[54] U. Franke and A. Joos, "Real-Time Stereo Vision for Urban Traffic Scene Understanding," Proc. IEEE Intelligent Vehicles Symp., pp. 273-278, 2000.
[55] D. Gavrila, J. Giebel, and S. Munder, "Vision-Based Pedestrian Detection: The PROTECTOR System," Proc. IEEE Intelligent Vehicles Symp., pp. 13-18, 2004.
[56] G. Grubb, A. Zelinsky, L. Nilsson, and M. Rilbe, "3D Vision Sensing for Improved Pedestrian Safety," Proc. IEEE Intelligent Vehicles Symp., pp. 19-24, 2004.
[57] S. Krotosky and M.M. Trivedi, "On Color-, Infrared-, and Multimodal-Stereo Approaches to Pedestrian Detection," IEEE Trans. Intelligent Transportation Systems, vol. 8, no. 4, pp. 619-629, Dec. 2007.
[58] L. Zhao and C. Thorpe, "Stereo and Neural Network-Based Pedestrian Detection," IEEE Trans. Intelligent Transportation Systems, vol. 1, no. 3, pp. 148-154, Sept. 2000.
[59] M. Soga, T. Kato, M. Ohta, and Y. Ninomiya, "Pedestrian Detection with Stereo Vision," Proc. IEEE Int'l Conf. Data Eng. Workshops, p. 1200, 2005.
[60] S. Krotosky and M.M. Trivedi, "Multimodal Stereo Image Registration for Pedestrian Detection," Proc. IEEE Int'l Conf. Intelligent Transportation Systems, pp. 109-114, 2007.
[61] H. Elzein, S. Lakshmanan, and P. Watta, "A Motion and Shape-Based Pedestrian Detection Algorithm," Proc. IEEE Intelligent Vehicles Symp., pp. 500-504, 2003.
[62] U. Franke and S. Heinrich, "Fast Obstacle Detection for Urban Traffic Situations," IEEE Trans. Intelligent Transportation Systems, vol. 3, no. 3, pp. 173-181, Sept. 2002.
[63] B. Leibe, N. Cornelis, K. Cornelis, and L. VanGool, "Dynamic 3D Scene Analysis from a Moving Vehicle," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1-8, 2007.
[64] H. Badino, W. Franke, and R. Mester, "Free Space Computation Using Stochastic Occupancy Grids and Dynamic Programming," Proc. Int'l Conf. Computer Vision, Workshop Dynamical Vision, 2007.
[65] W. vander Mark and D. Gavrila, "Real-Time Dense Stereo for Intelligent Vehicles," IEEE Trans. Intelligent Transportation Systems, vol. 7, no. 1, pp. 38-50, Mar. 2006.
[66] B. Hidalgo-Sotelo, A. Oliva, and A. Torralba, "Human Learning of Contextual Priors for Object Search," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 86-93, 2005.
[67] A. Torralba and A. Oliva, "The Role of Context in Object Recognition," Trends in Cognitive Sciences, vol. 11, no. 12, pp. 520-527, 2007.
[68] D. Hoiem, A. Efros, and M. Hebert, "Closing the Loop in Scene Interpretation," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[69] D. Gavrila, "Pedestrian Detection from a Moving Vehicle," Proc. European Conf. Computer Vision, vol. 2, pp. 37-49, 2000.
[70] D. Gavrila and S. Munder, "Multi-Cue Pedestrian Detection and Tracking from a Moving Vehicle," Int'l J. Computer Vision, vol. 73, no. 1, pp. 41-59, 2007.
[71] H. Nanda and L. Davis, "Probabilistic Template Based Pedestrian Detection in Infrared Videos," Proc. IEEE Intelligent Vehicles Symp., pp. 15-20, 2002.
[72] V. Vapnik, The Nature of Statistical Learning Theory. Springer, 1995.
[73] F. Xu, X. Liu, and K. Fujimura, "Pedestrian Detection and Tracking with Night Vision," IEEE Trans. Intelligent Transportation Systems, vol. 6, no. 1, pp. 63-71, Mar. 2005.
[74] A. Mohan, C. Papageorgiou, and T. Poggio, "Example-Based Object Detection in Images by Components," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 4, pp. 349-361, Apr. 2001.
[75] L. Andreone, F. Bellotti, A.D. Gloria, and R. Lauletta, "SVM-Based Pedestrian Recognition on Near-Infrared Images," Proc. Fourth Int'l Symp. Image and Signal Processing and Analysis, pp. 274-278, 2005.
[76] L. Zhang, B. Wu, and R. Nevatia, "Pedestrian Detection in Infrared Images Based on Local Shape Features," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1-8, 2007.
[77] C. Wojek and B. Schiele, "A Performance Evaluation of Single and Multi-Feature People Detection," Proc. DAGM Symp., pp. 82-91, 2008.
[78] Y. Freund and R. Schapire, "A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting," J. Computer and System Sciences, vol. 55, no. 1, pp. 119-139, 1997.
[79] P. Viola, M. Jones, and D. Snow, "Detecting Pedestrians Using Patterns of Motion and Appearance," Proc. Int'l Conf. Computer Vision, vol. 2, pp. 734-741, 2003.
[80] R. Schapire and Y. Singer, "Improved Boosting Algorithms Using Confidence-Rated Predictions," Machine Learning, vol. 37, no. 3, pp. 297-336, 1999.
[81] J. Friedman, T. Hastie, and R. Tibshirani, "Additive Logistic Regression: A Statistical View of Boosting," Annals of Statistics, vol. 28, no. 2, pp. 337-407, 2000.
[82] C. Huang, H. Ai, B. Wu, and S. Lao, "Boosting Nested Cascade Detector for Multi-View Face Detection," Proc. Int'l Conf. Pattern Recognition, vol. 2, pp. 415-418, 2004.
[83] X. Xu and E. Frank, "Logistic Regression and Boosting for Labeled Bags of Instances," Proc. Pacific Asia Conf. Knowledge Discovery and Data Mining, 2004.
[84] B. Wu and R. Nevatia, "Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet Part Detectors," Int'l J. Computer Vision, vol. 75, no. 2, pp. 247-266, 2007.
[85] M. Szarvas, A. Yoshizawa, M. Yamamoto, and J. Ogata, "Pedestrian Detection with Convolutional Neural Networks," Proc. IEEE Intelligent Vehicles Symp., pp. 224-229, 2005.
[86] C. Bishop, Neural Networks for Pattern Recognition. Oxford Univ. Press, 1995.
[87] S. Munder and D. Gavrila, "An Experimental Study on Pedestrian Classification," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 11, pp. 1863-1868, Nov. 2006.
[88] M. Jones and D. Snow, "Pedestrian Detection Using Boosted Features over Many Frames," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[89] K. Levi and Y. Weiss, "Learning Object Detection from a Small Number of Examples: The Importance of Good Features," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 53-60, 2004.
[90] D. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," Int'l J. Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.
[91] N. Dalal, B. Triggs, and C. Schmid, "Human Detection Using Oriented Histograms of Flow and Appearance," Proc. European Conf. Computer Vision, pp. 428-441, 2006.
[92] J. Pang, Q. Huang, and S. Jiang, "Multiple Instance Boost Using Graph Embedding Based Decision Stump for Pedestrian Detection," Proc. European Conf. Computer Vision, vol. 4, pp. 541-552, 2008.
[93] S. Maji, A. Berg, and J. Malik, "Classification Using Intersection Kernel Support Vector Machines Is Efficient," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[94] B. Wu and R. Nevatia, "Simultaneous Object Detection and Segmentation by Boosting Local Shape Feature Based Classifier," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1-8, 2007.
[95] P. Sabzmeydani and G. Mori, "Detecting Pedestrians by Learning Shapelet Features," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1-8, 2007.
[96] O. Tuzel, F. Porikli, and P. Meer, "Pedestrian Detection via Classification on Riemannian Manifold," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 10, pp. 1713-1727, Oct. 2008.
[97] A. Shashua, Y. Gdalyahu, and G. Hayun, "Pedestrian Detection for Driving Assistance Systems: Single-Frame Classification and System Level Performance," Proc. IEEE Intelligent Vehicles Symp., pp. 1-6, 2004.
[98] I. Parra, D. Fernández, M. Sotelo, L. Bergasa, P. Revenga, J. Nuevo, M. Ocana, and M.A. García, "Combination of Feature Extraction Method for SVM Pedestrian Detection," IEEE Trans. Intelligent Transportation Systems, vol. 8, no. 2, pp. 292-307, June 2007.
[99] D. Tran and D. Forsyth, "Configuration Estimates Improve Pedestrian Finding," Proc. Conf. Neural Information Processing Systems Conf., pp. 1529-1536, 2007.
[100] P. Felzenszwalb, D. McAllester, and D. Ramanan, "A Discriminatively Trained, Multiscale, Deformable Part Model," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[101] P. Dollár, B. Babenko, S. Belongie, P. Perona, and Z. Tu, "Multiple Component Learning for Object Detection," Proc. European Conf. Computer Vision, pp. 211-224, 2008.
[102] Z. Lin and L. Davis, "A Pose-Invariant Descriptor for Human Detection and Segmentation," Proc. European Conf. Computer Vision, vol. 4, pp. 423-436, 2008.
[103] B. Leibe, A. Leonardis, and B. Schiele, "Robust Object Detection with Interleaved Categorization and Segmentation," Int'l J. Computer Vision, vol. 77, nos. 1-3, pp. 259-289, 2008.
[104] K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. Van Gool, "A Comparison of Affine Region Detectors," Int'l J. Computer Vision, vol. 65, nos. 1/2, pp. 43-72, 2005.
[105] S. Belongie, J. Malik, and J. Puzicha, "Shape Matching and Object Recognition Using Shape Contexts," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 4, pp. 509-522, Apr. 2002.
[106] E. Seeman, B. Leibe, and B. Schiele, "Multi-Aspect Detection of Articulated Objects," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 1582-1588, 2006.
[107] E. Seeman and B. Schiele, "Cross-Articulation Learning of Robust Detection of Pedestrians," Proc. DAGM Symp., 2006.
[108] L. Zhang and R. Nevatia, "Efficient Scan-Window Based Object Detection Using GPGPU," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[109] B. Leibe, E. Seemann, and B. Schiele, "Pedestrian Detection in Crowded Scenes," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 878-885, 2005.
[110] N. Dalal, "Finding People in Images and Videos," PhD thesis, Inst. Nat'l Polytechnique de Grenoble/INRIA Rhône-Alpes, 2006.
[111] D. Comaniciu, "An Algorithm for Data-Driven Bandwidth Selection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 2, pp. 281-288, Feb. 2003.
[112] S. Agarwal, A. Awan, and D. Roth, "Learning to Detect Objects in Images via a Sparse, Part-Based Representation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 11, pp. 1475-1490, Nov. 2004.
[113] A. Broggi, A. Fascioli, P. Grisleri, T. Graf, and M.-M. Meinecke, "Model-Based Validation Approaches and Matching Techniques for Automotive Vision Based Pedestrian Detection," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 3, p. 1, 2005.
[114] M. Bertozzi, A. Broggi, M. DelRose, and M. Felisa, "A Symmetry-Based Validator and Refinement System for Pedestrian Detection in Far Infrared Images," Proc. IEEE Int'l Conf. Intelligent Transportation Systems, pp. 155-160, 2007.
[115] G. Welch and G. Bishop, "An Introduction to The Kalman Filter," technical report, Dept. of Computer Science, Univ. of North Carolina at Chapel Hill, 2002.
[116] E. Binelli, A. Broggi, A. Fascioli, S. Ghidoni, P. Grisleri, T. Graf, and M.-M. Meinecke, "A Modular Tracking System for Far Infrared Pedestrian Recognition," Proc. IEEE Intelligent Vehicles Symp., pp. 759-764, 2005.
[117] J. Giebel, D. Gavrila, and C. Schnör, "A Bayesian Framework for Multi-Cue 3D Object Tracking," Proc. European Conf. Computer Vision, pp. 241-252, 2004.
[118] V. Philomin, R. Duraiswami, and L. Davis, "Pedestrian Tracking from a Moving Vehicle," Proc. IEEE Intelligent Vehicles Symp., pp. 350-355, 2000.
[119] M. Isard and A. Blake, "Contour Tracking by Stochastic Propagation of Conditional Density," Proc. European Conf. Computer Vision, pp. 343-356, 1996.
[120] R. Arndt, R. Schweiger, W. Ritter, D. Paulus, and O. Löhlein, "Detection and Tracking of Multiple Pedestrians in Automotive Applications," Proc. IEEE Intelligent Vehicles Symp., pp. 13-18, 2007.
[121] P. Viola and M. Jones, "Rapid Object Detection Using a Boosted Cascade of Simple Features," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 511-518, 2001.
[122] O. Mateo and K. Otsuka, "Real-Time Visual Tracker by Stream Processing," J. Signal Processing Systems, 2008.
[123] L. Zhang, Y. Li, and R. Nevatia, "Global Data Association for Multi-Object Tracking Using Network Flows," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[124] S. Gammeter, A. Ess, T. Jäggli, K. Schindler, B. Leibe, and L. VanGool, "Articulated Multi-Body Tracking under Egomotion," Proc. European Conf. Computer Vision, 2008.
[125] M. Adnriluka, S. Roth, and B. Schiele, "People-Tracking-by-Detection and People-Detection-by-Tracking," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[126] V. Singh, B. Wu, and R. Nevatia, "Pedestrian Tracking by Associating Tracklets Using Detection Residuals," Proc. Workshop Motion and Video Computing, pp. 1-8, 2008.
[127] B. Leibe, A. Leonardis, and B. Schiele, "Combined Object Categorization and Segmentation with an Implicit Shape Model," Proc. European Conf. Computer Vision Workshop Statistical Learning in Computer Vision, pp. 17-32, 2004.
[128] B. Leibe and B. Schiele, "Scale-Invariant Object Categorization Using a Scale-Adaptive Mean-Shift Search," Proc. DAGM Symp., pp. 145-153, 2004.
[129] P. Marchal, M. Dehesa, D. Gavrila, M.-M. Meinecke, N. Skellern, and R. Viciguerra, "SAVE-U. Final Report," technical report, Information Soc. Technology Programme of the EU, 2005.
[130] T. Graf, K. Seifert, M.-M. Meinecke, and R. Schmidt, "Human Factors in Designing Advanced Night Vision Systems," Proc. Fifth Congress and Exhibition on Intelligent Transport Systems and Services, 2005.
[131] C.-Y. Chan and F. Bu, "Literature Review of Pedestrian Detection Technologies and Sensor Survey," technical report, Inst. of Transportation Studies, Univ. of California at Berkeley, 2005.
[132] E. Goubet, J. Katz, and F. Porikli, "Pedestrian Tracking Using Thermal Infrared Imaging," Proc. SPIE Conf. Infrared Technology and Applications, pp. 797-808, 2006.
[133] B. Fardi, U. Schuenert, and G. Wanielik, "Shape and Motion-Based Pedestrian Detection in Infrared Images: A Multi Sensor Approach," Proc. IEEE Intelligent Vehicles Symp., pp. 18-23, 2005.
[134] C. Premebida, G. Monteiro, U. Nunes, and P. Peixoto, "A Lidar and Vision-Based Approach for Pedestrian and Vehicle Detection and Tracking," Proc. IEEE Int'l Conf. Intelligent Transportation Systems, pp. 1044-1049, 2007.
[135] S. Milch and M. Behrens, "Pedestrian Detection with Radar and Computer Vision," Proc. Conf. Progress in Automobile Lighting, 2001.
[136] D. Linzmeier, M. Skutek, M. Mekhaiel, and K. Dietmayer, "A Pedestrian Detection System Based on Thermopile and Radar Sensor Data Fusion," Proc. Int'l Conf. Information Fusion, vol. 2, 2005.
[137] M. Bertozzi, A. Broggi, M. Felisa, G. Vezzoni, and M. DelRose, "Low-Level Pedestrian Detection by Means of Visible and Far Infra-Red Tetra-Vision," Proc. IEEE Intelligent Vehicles Symp., pp. 231-236, 2006.
[138] H. Sun, C. Hua, and Y. Luo, "A Multi-Stage Classifier Based Algorithm of Pedestrian Detection in Night with a Near Infrared Camera in a Moving Car," Proc. Third Int'l Conf. Image and Graphics, pp. 120-123, 2004.
[139] M. Bertozzi, A. Broggi, C. Hilario, R. Fedriga, G. Vezzoni, and M.D. Rose, "Pedestrian Detection in Far Infrared Images Based on the Use of Probabilistic Templates," Proc. IEEE Intelligent Vehicles Symp., pp. 327-332, 2007.
[140] M. Enzweiler and D. Gavrila, "A Mixed Generative-Discriminative Framework for Pedestrian Classification," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[141] J. David and M. Keck, "A Two-Stage Approach to Person Detection in Thermal Imagery," Proc. Workshop Applications of Computer Vision, vol. 1, pp. 364-369, 2005.
[142] C. Wojek, S. Walk, and B. Schiele, "Multi-Cue Onboard Pedestrian Detection," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
[143] P. Dollár, C. Wojek, B. Schiele, and P. Perona, "Pedestrian Detection: A Benchmark," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
[144] A.-S. Karlsson et al., "Deliverable D50.30. User Needs State of the Art and Relevance for Accidents. PReVENT Project Apalaci: Preventive and Active Safety Applications," 2005.
[145] A. López, J. Hilgenstock, A. Busse, R. Baldrich, F. Lumbreras, and J. Serrat, "Nighttime Vehicle Detection for Intelligent Headlight Control," Proc. Int'l Conf. Advanced Concepts for Intelligent Vision Systems, pp. 113-124, 2008.
[146] S. Park and M. Trivedi, "Driver Activity Analysis for Intelligent Vehicles: Issues and Development Framework," Proc. IEEE Intelligent Vehicles Symp., pp. 644-649, 2005.
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