M. Bertozzi, A. Broggi, A. Fascioli, and M. Sechi, Shape-based Pedestrian Detection, Procs. IEEE Intelligent Vehicles Symposium, pp.215-220, 2000.

M. Bertozzi, A. Broggi, M. Felisa, G. Vezzoni, and M. D. Rose, Low-level Pedestrian Detection by means of Visible and Far Infra-red Tetra-vision, 2006 IEEE Intelligent Vehicles Symposium, pp.231-236, 2006.
DOI : 10.1109/IVS.2006.1689633

D. Beymer and K. Konolige, Real-time Tracking of Multiple People using Continuous Detection, Procs. Intl. Conf. on Computer Vision, 1999.

A. Broggi, M. Bertozzi, M. Felisa, P. Grisleri, S. Ghidoni et al., Pedestrian Detection by means of Far-infrared Stereo Vision, Computer Vision and Image Understanding, vol.106, issue.2, pp.194-204, 2007.

C. Curio, J. Edelbrunner, T. Kalinke, C. Tzomakas, and W. Von-seelen, Walking pedestrian recognition, IEEE Transactions on Intelligent Transportation Systems, vol.1, issue.3, pp.155-163, 2000.
DOI : 10.1109/6979.892152

R. Cutler and L. S. Davis, Robust real-time periodic motion detection, analysis, and applications, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.8, pp.781-796, 2000.
DOI : 10.1109/34.868681

M. Dao, F. G. Natale, and A. Massa, Edge potential functions and genetic algorithms for shape-based image retrieval, Procs. IEEE Intl. Conf. on Image Processing (ICIP'03), pp.729-732, 2003.

M. Dao, F. G. Natale, and A. Massa, Efficient Shape Matching Using Weighted Edge Potential Functions, Procs. 13 th Intl. Conf. on Image Analysis and Processing (ICIAP'05), 2005.
DOI : 10.1007/11553595_77

M. , D. Rose, and P. Frederick, Pedestrian Detection, Procs. Intelligent Vehicle Systems Symposium, 2005.

D. M. Gavrila, Pedestrian Detection from a Moving Vehicle, Procs. of European Conference on Computer Vision, pp.37-49, 2000.
DOI : 10.1007/3-540-45053-X_3

R. Kania, M. D. Rose, and P. Frederick, Autonomous Robotic Following Using Vision Based Techniques, Procs. Ground Vehicle Survivability Symposium, 2005.

M. Kass, A. Witkin, and D. Terzopoulos, Snakes: Active contour models, International Journal of Computer Vision, vol.5, issue.6035, pp.321-331, 1988.
DOI : 10.1007/BF00133570

H. Nanda and L. Davis, Probabilistic template based pedestrian detection in infrared videos, Intelligent Vehicle Symposium, 2002. IEEE, 2002.
DOI : 10.1109/IVS.2002.1187921

V. Philomin, R. Duraiswami, and L. Davis, Pedestrian tracking from a moving vehicle, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511), pp.350-355, 2000.
DOI : 10.1109/IVS.2000.898368

R. Polana and R. C. Nelson, Detection and Recognition of Periodic, Non-rigid Motion, International Journal of Computer Vision, vol.23, issue.3, pp.261-282, 1997.
DOI : 10.1023/A:1007975200487

A. Shashua, Y. Gdalyahu, and G. Hayun, Pedestrian detection for driving assistance systems: single-frame classification and system level performance, IEEE Intelligent Vehicles Symposium, 2004
DOI : 10.1109/IVS.2004.1336346

H. Shimizu and T. Poggie, Direction estimation of pedestrian from multiple still images, IEEE Intelligent Vehicles Symposium, 2004, 2004.
DOI : 10.1109/IVS.2004.1336451

C. Stauffer and W. E. Grimson, Similarity templates for detection and recognition, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, pp.221-228, 2001.
DOI : 10.1109/CVPR.2001.990479

D. J. Williams and M. Shah, A Fast algorithm for active contours and curvature estimation, CVGIP: Image Understanding, vol.55, issue.1, pp.14-26, 1992.
DOI : 10.1016/1049-9660(92)90003-L

L. Zhao, Dressed Human Modeling, Detection, and Parts Localization, 2001.

L. Zhao and C. Thorpe, Stereo- and neural network-based pedestrian detection, IEEE Transactions on Intelligent Transportation Systems, vol.1, issue.3, pp.148-154, 2000.
DOI : 10.1109/6979.892151