Incremental learning for motion prediction of pedestrians and vehicles

The monograph written by Alejandro Dizan Vasquez Govea focuses on the practical problem of moving in a cluttered environment with pedestrians and vehicles.A framework based on Hidden Markov models is developed to learn typical motion patterns which can be used to predict motion on the basis of sensor data. All the theoretical results have been implemented and validated with experiments, using both real and simulated data. Remarkably, the monograph is based on the author’s doctoral thesis, which received the prize of the Eight Edition of the EURON Georges Giralt PhD Award devoted to the best PhD thesis in Robotics in Europe. A very fine addition to STAR! Recent research in the area of motion prediction of Pedestrians and Vehicles Presents the modeling, learning and prediction of motion Based on the winning thesis of the EURON Georges Giralt award INDICE: Part I Background.- Probabilistic Models.- Part II State of the Art.- Intentional Motion Prediction.- Hidden Markov Models.- Part III Proposed Approach.- Growing Hidden Markov Models.- Learning and Predicting Motion with GHMMs.- Part IV Experiments.- Experimental Data.- Experimental Results.- Part VConclusion.- Conclusions and Future Work.

• ISBN: 978-3-642-13641-2
• Editorial: Springer
• Encuadernacion: Cartoné
• Páginas: 160
• Fecha Publicación: 23/06/2010
• Nº Volúmenes: 1
• Idioma: Inglés