L. Itti and C. Koch, Computational modeling of visual attention, Nature Reviews Neuroscience, vol.2, issue.3, pp.194-203, 2001.
DOI : 10.1038/35058500

A. Torralba, Contextual priming for object detection, 2003.

S. Frintrop, E. Backer, and . Rome, Goal-Directed Search with a Top-Down Modulated Computational Attention System, Pattern Recognition, 2005.
DOI : 10.1007/11550518_15

T. Michalke, A. Gepperth, M. Schneider, J. Fritsch, and C. Goerick, Towards a human-like vision system for resource-constrained intelligent cars, The 5th Int. Conf. on Computer Vision Systems Conference. Universit.tsbibliothek Bielefeld, 2007.

M. Mary, T. Hayhoe, K. Mckinney, J. B. Chajka, and . Pelz, Predictive eye movements in natural vision, Experimental brain research, pp.1-12, 2012.

M. Enzweiler, A. Eigenstetter, B. Schiele, and D. M. Gavrila, Multicue pedestrian classification with partial occlusion handling, pp.990-997, 2010.

M. Enzweiler and D. M. Gavrila, Integrated pedestrian classification and orientation estimation, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.982-989, 2010.
DOI : 10.1109/CVPR.2010.5540110

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.170.3234

T. Gandhi and M. M. Trivedi, Pedestrian Protection Systems: Issues, Survey, and Challenges, IEEE Transactions on Intelligent Transportation Systems, vol.8, issue.3, pp.413-430, 2007.
DOI : 10.1109/TITS.2007.903444

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.81.6405

B. Schiele, P. Dollár, C. Wojek, and P. Perona, Pedestrian detection: A benchmark, Computer Vision and Pattern Recognition (CVPR), 2009.

N. Dalal and B. Triggs, Histograms of Oriented Gradients for Human Detection, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.886-893, 2005.
DOI : 10.1109/CVPR.2005.177

URL : https://hal.archives-ouvertes.fr/inria-00548512

P. Dollár, S. Belongie, and P. Perona, The Fastest Pedestrian Detector in the West, Procedings of the British Machine Vision Conference 2010, 2010.
DOI : 10.5244/C.24.68

B. Schiele, P. Dollar, C. Wojek, and P. Perona, Pedestrian detection: An evaluation of the state of the art, 2011.

D. Musicki and B. L. Scala, Multi-target tracking in clutter without measurement assignment. Aerospace and Electronic Systems, IEEE Transactions on, vol.44, issue.3, pp.877-896, 2008.

C. Hoffman, T. Dang, and C. Stiller, Vehicle detection fusing 2d visual features, Intelligent Vehicles Symposium, pp.280-285, 2004.

U. Franke and C. Rabe, Kalman filter based depth from motion with fast convergence, IEEE Proceedings. Intelligent Vehicles Symposium, 2005., pp.181-186, 2005.
DOI : 10.1109/IVS.2005.1505099

X. Clady, F. Collange, F. Jurie, and P. Martinet, Cars detection and tracking with a vision sensor, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683), pp.593-598, 2003.
DOI : 10.1109/IVS.2003.1212979

URL : https://hal.archives-ouvertes.fr/inria-00548237

J. Schmuedderich, N. Einecke, S. Hasler, A. Gepperth, B. Bolder et al., System approach for multi-purpose representations of traffic scene elements, 13th International IEEE Conference on Intelligent Transportation Systems, 2010.
DOI : 10.1109/ITSC.2010.5625234

D. Simon, Optimal state estimation: Kalman, H infinity, and nonlinear approaches, 2006.
DOI : 10.1002/0470045345

H. Sidenbladh, Multi-target particle filtering for the probability hypothesis density, Sixth International Conference of Information Fusion, 2003. Proceedings of the, 2003.
DOI : 10.1109/ICIF.2003.177321

V. Navalpakkam and L. Itti, An integrated model of top-down and bottom-up attention for optimal object detection, Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2049-2056, 2006.

J. Wolfe, Guided Search 2.0 A revised model of visual search, Psychonomic Bulletin & Review, vol.18, issue.2, pp.202-238, 1994.
DOI : 10.3758/BF03200774

M. W. Spratling, Predictive coding as a model of biased competition in visual attention, Vision Research, vol.48, issue.12, pp.1391-1408, 2008.
DOI : 10.1016/j.visres.2008.03.009

H. Fred and . Hamker, Modeling feature-based attention as an active topdown inference process, Biosystems, vol.86, issue.1-3, pp.91-99, 2006.

G. Deco, T. Edmund, and . Rolls, A Neurodynamical cortical model of visual attention and invariant object recognition, Vision Research, vol.44, issue.6, pp.621-642, 2004.
DOI : 10.1016/j.visres.2003.09.037

T. Michalke, A. Gepperth, M. Schneider, J. Fritsch, and C. Goerick, Towards a human-like vision system for resource-constrained intelligent cars, The 5th International Conference on Computer Vision Systems, 2007.

A. Gepperth, S. Rebhan, S. Hasler, and J. Fritsch, Biased Competition in Visual Processing Hierarchies: A Learning Approach Using Multiple Cues, Cognitive Computation, vol.10, issue.2, pp.146-166, 2011.
DOI : 10.1007/s12559-010-9092-x

URL : https://hal.archives-ouvertes.fr/hal-00647809

B. Dittes, M. Heracles, T. Michalke, R. Kastner, A. Gepperth et al., A Hierarchical System Integration Approach with Application to Visual Scene Exploration for Driver Assistance, ICVS, 2009.
DOI : 10.1007/978-3-642-04667-4_26

B. Leibe, . Cornelis, . Cornelis, and . Van-gool, Dynamic 3D Scene Analysis from a Moving Vehicle, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2007.383146

G. Bradski and A. Kaehler, Learning OpenCV: Computer vision with the OpenCV library, 2008.

A. Geiger, P. Lenz, and R. Urtasun, Are we ready for autonomous driving? The KITTI vision benchmark suite, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.3354-3361, 2012.
DOI : 10.1109/CVPR.2012.6248074