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Pedestrian Detection and Analysis with Scale-Space and Distance Transform

-1963, September 2004 [6] D.M. Gavrila, V. Philomin, Real-time Object Detection for Smart Vehicles, Proc. of IEEE International Conference on Computer Vision, Kerkyra, Greece, pp. 87-93, 1999 [7] B.K.P. Horn, B.G. Schunck, Determining optical flow, Artificial Intelligence, Vol 17, pp 185-203, 1981 [8] I. Laptev, T Lindeberg, Tracking of Multi-state Hand Models Using Particle Filtering and a Hierarchy of Multi-scale Image Features, In Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision

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Macroscopic Traffic Flow Control Via State Estimation

Conference on Control and Automation (MED.07), 2007. http://hal.inria.fr/inria/00142588/fr/ 17. Mihaylova, L., R. B o e l. A Particle Filter for Freeway Traffic Estimation. - In: Proc. of 43rd IEEE Conf. on Decision and Control, 14-17 December 2004, Atlantis, Paradise Island, Bahamas, pp. 2106-2111. 18. Nahi, N.E., A. N. Trivedi. Recursive Estimation of Traffic Variables: Section Density and Average Speed. Tech. Report, 1976. University of Southern California, Los Angeles. 19. Rolink, M., T. Boukhobza, D. Sauter. High Order

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Landmark Sequence Data Association for Simultaneous Localization and Mapping of Robots

References 1. Uyen, H. S. V., J. W. Jeon. Combine Kalman Filter and Particle Filter to Improve Color Tracking Algorithm. - In: Proc. of International Conference on Control, Automation and Systems 2007, 558-561. 2. Anati, R., D. Scar amu z z a, K. G. Derpanis, K. Daniilidis. Robot Localization Using Soft Object Detection. - In: Proc. of IEEE International Conference on Robotics and Automation (ICRA’2012), 2012, 4992-4999. 3. Ivanjko, E., M. Uasak, I. Petrovic. Kalman Filter Theory Based Mobile Robot Pose Tracking

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Multi-Targets Tracking Based On Bipartite Graph Matching

, 1048-1058. 16. Duchenne, O., F. Bach, Kweon In-So, J. Ponce. A Tensor-Based Algorithm for High- Order Graph Matching. - IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 33, 2011, No 12, 2383-2395. 17. Taeg, Sang Cho, S. Avidan, W. T. Freeman. A Probabilistic Image Jigsaw Puzzle Solver. - In: IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, 2011, 183-190. 18. Yang, Xingwei, N. Adluru, L. J. Latechi. Particle Filter with State Permutations for Solving Image Jigsaw Puzzles

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Algebraic Methods for Traffic Flow Densities Estimation

, University of Toronto, Canada, 2001. 23. Papageorgiou, M., G. Vigo s. Relating Time-Occupancy Measurements to Space- Occupancy and Link Vehicle-Count. - Transportation Research, Part C, Vol. 16, 2008, 1-17. 24. Hegyi, A., D. Girimont e, R. Babusk a, B. De Schutter. A Comparison of Filter Configurations for Freeway Traffic State Estimation. - In: Proc. Conf. IEEE ITSC 2006, 17-20 September 2006, Toronto, Canada. 25. Mihaylov a, L., R. Boel. A Particle Filter for Freeway Traffic Estimation. - In: Proc. of 43rd IEEE Conf. on

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The Characteristics of the Map Merging Methods: A Survey

. 1308-1313. N. Ceccarelli, et al., "Set membership localization and map building for mobile robots," in Systems & Control: Foundations & Applications , Part III. Boston: Birkhauser, 2006, pp. 289-308. A. Howard, "Multi-robot simultaneous localization and mapping using particle filters," The International Journal on Robotics Research , vol. 25, no. 12. Sage Science Press, 2006, pp. 1243-1256. P. Bergasa, et al., "SLAM and map merging," Journal of Physical Agents , vol. 3, no. 1, 2009

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