Fusión de Escáner Laser y Visión por Computador para la Detección de Peatones en Entornos Viarios
Enviado: 30-01-2018
|Aceptado: 30-01-2018
|Descargas
Palabras clave:
Fusión de Información, Visión por Computador, Máquinas Inteligentes, Vehículos, Detección de Obstáculos
Agencias de apoyo:
CICYT (GRANT TRA2013-48314-C3-1-R)
y (GRANT TRA 2011-29454-C03- 02).
Resumen:
Citas:
Bertozzi, M., Broggi, et. al., 2009. Multi Stereo-based Pedestrian Detection by means of Daylight and Far Infrared Cameras. In: R. I. Hammoud (Ed.), Object Tracking and Classification Beyond the Visible Spectrum. Springer-Verlag. pp. 371–401.
Blackman, S., Popoli, R., 1999 Design and Analysis of Modern Tracking Systems. Norwood MA Artech House. Artech House, Norwood, MA.
Blackman, S. S. ,1986. Multiple-Target Tracking with Radar Application. Dedham MA Artech House Inc. Artech House, Norwood, MA.
Broggi, A., Cerri, P., Ghidoni, S., Grisleri, P., Jung, H. G. ,2008. Localization and Analysis of Critical Areas in Urban Scenarios. In IEEE Intelligent Vehicles Symposium, pp. 1074–1079. DOI:10.1109/IVS.2008.4621266
Dalal, N., Triggs, B. ,2005. Histograms of Oriented Gradients for Human Detection. Computer Vision and Pattern Recognition, 2005. 1, 886– 893.
Direccion General de Tráfico, 2011. Anuario Estadístico de General. Dirección General de Tráfico. Ministerio del Interior. (D. G. de Tráfico, Ed.). Madrid.
Fan, X., Mittal, S., Prasad, T., Saurabh, S., Shin, H. ,2013. Pedestrian Detection and Tracking Using Deformable Part Models and Kalman Filtering. Journal of Communication and Computer, 10, 960–966.
García, F., Jiménez, F., Naranjo, J. E., Aparicio, F., Zato, J. G., & Escalera, a. D. La. ,2011. Laser Scanner Como Sistema de Detección de Entornos Viales. Revista Iberoamericana de Automática E Informática Industrial, 8(1), 44–53. DOI:10.4995/RIAI.2011.01.07
Highway Capacity Manual 2000, 2000. Board. Transportation Research Board, National Academy of Sciences.
Hwang, J. P., Cho, S. E., Ryu, K. J., Park, S., & Kim, E. (2007). MultiClassifier Based LIDAR and Camera Fusion. IEEE Intelligent Transportation Systems Conference ITSC, 467–472. DOI:10.1109/ITSC.2007.4357683
Kohler, M., 1997. Using the Kalman Filter to track Human Interactive Motion - Modelling and Initialization of the Kalman Filter for Translational Motion.
Li, D., Xu, L., Goodman, E. D., Xu, Y., Wu, Y. ,2013. Integrating a Statistical Background-Foreground Extraction Algorithm and SVM Classifier for Pedestrian Detection and Tracking. Integrated Computer-Aided Engineering, 20(3), 201–216.
Ludwig, O., Premebida, C., Nunes, U., Ara, R. ,2011. Evaluation of BoostingSVM and SRM-SVM Cascade Classifiers in Laser and Vision-based Pedestrian Detection. In IEEE Intelligent Transportation Systems Conference ITSC (pp. 1574–1579).
Pérez Grassi, A., Frolov, V., Puente León, F, 2010. Information fusion to detect and classify pedestrians using invariant features. Information Fusion, 12(4), 284–292.
Premebida, C., Ludwig, O., & Nunes, U.,2009. LIDAR and Vision-Based Pedestrian Detection System. Journal of Field Robotics, 26(Iv), 696– 711.
Premebida, C., Ludwig, O., Silva, M., Nunes, U., 2010. A Cascade Classifier applied in Pedestrian Detection using Laser and Image-based Features. IEEE Intelligent Transportation Systems Conference ITSC, 1153– 1159.
Premebida, C., Monteiro, G., Nunes, U., Peixoto, P,2007. A Lidar and Visionbased Approach for Pedestrian and Vehicle Detection and Tracking. IEEE Intelligent Transportation Systems Conference ITSC, 1044– 1049. DOI:10.1109/ITSC.2007.4357637
Premebida, C., Nunes, U. J. C., 2013. Fusing LIDAR, Camera and Semantic Information: A context-based approach for pedestrian detection. The International Journal of Robotics Research.
Schneider, N., & Gavrila, D. M. ,2013. Pedestrian Path Prediction with Recursive Bayesian Filters: A Comparative Study. In Pattern Recognition. Springer. pp. 174–183
Skehill, R. J., Barry, M., Mcgrath, S., 2005. Mobility Modelling with Empirical Pedestrian and Vehicular Traffic Characteristics. WSEAS Transactions on Communications, 4(10).
Spinello, L., & Siegwart, R. ,2008. Human Detection Using Multimodal and Multidimensional Features. 2008 IEEE International Conference on Robotics and Automation, 3264–3269. DOI:10.1109/ROBOT.2008.4543708
Still, G. K., 2000. Crowd dynamics. Philosophy. University of Warwick.
Szarvas, M., & Sakai, U., 2006. Real-time Pedestrian Detection Using LIDAR and Convolutional Neural Networks. In EEE Intelligent Vehicles Symposium (pp. 213–218).



