Incremental Learning for Motion Prediction of Pedestrians and Vehicles - Couverture rigide
Livre 57 sur 153: Springer Tracts in Advanced Robotics
Synopsis
Roboticsis undergoingamajortransformationinscopeanddimension.From a largelydominantindustrialfocus, roboticsis rapidly expandinginto human environments and vigorouslyengaged in its new challenges. Interacting with, assisting, serving, and exploring with humans, the emerging robots will - creasingly touch people and their lives. Beyond its impact on physical robots, the body of knowledge robotics has produced is revealing a much wider range of applications reaching across - verse research areas and scienti?c disciplines, such as: biomechanics, haptics, neurosciences, virtual simulation, animation, surgery, and sensor networks among others. In return, the challenges of the new emerging areas are pr- ing an abundant source of stimulation and insights for the ?eld of robotics. It is indeed at the intersection of disciplines that the most striking advances happen. TheSpringerTractsinAdvancedRobotics(STAR)isdevotedtobringingto the research community the latest advances in the robotics ?eld on the basis of their signi?cance and quality. Through a wide and timely dissemination of critical research developments in robotics, our objective with this series is to promotemoreexchangesandcollaborationsamongtheresearchersinthec- munity and contributeto further advancements inthis rapidlygrowing?eld. The monographwritten byAlejandro DizanVasquez Goveafocusesonthe practicalproblem of moving in a cluttered environment with pedestrians and vehicles. A frameworkbased 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.
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Incremental Learning for Motion Prediction of Pedestrians and Vehicles (eng)
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Incremental Learning for Motion Prediction of Pedestrians and Vehicles
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Springer Berlin Heidelberg Jun 2010, 2010
ISBN 10 : 3642136419
ISBN 13 : 9783642136412
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Modeling and predicting human and vehicle motion is an active research domain.Owing to the difficulty in modeling the various factors that determine motion(e.g. internal state, perception) this is often tackled by applying machinelearning techniques to build a statistical model, using as input a collectionof trajectories gathered through a sensor (e.g. camera, laser scanner), and thenusing that model to predict further motion. Unfortunately, most currenttechniques use offline learning algorithms, meaning that they are not able tolearn new motion patterns once the learning stage has finished.This books presents a lifelong learning approach where motion patterns can belearned incrementally, and in parallel with prediction. The approach is based ona novel extension to hidden Markov models, and the main contribution presentedin this book, called growing hidden Markov models, which gives us the ability tolearn incrementally both the parameters and the structure of the model. Theproposed approach has been extensively validated with synthetic and realtrajectory data. In our experiments our approach consistently learned motionmodels that were more compact and accurate than those produced by two otherstate-of-the-art techniques, confirming the viability of lifelong learningapproaches to build human behavior models. 176 pp. Englisch. N° de réf. du vendeur 9783642136412
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Incremental Learning for Motion Prediction of Pedestrians and Vehicles (Springer Tracts in Advanced Robotics, 64)
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Incremental Learning for Motion Prediction of Pedestrians and Vehicles
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ISBN 13 : 9783642136412
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Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Recent research in the area of motion prediction of Pedestrians and VehiclesPresents the modeling, learning and prediction of motionBased on the winning thesis of the EURON Georges Giralt awardI: Background.- Probabilistic Models.- . N° de réf. du vendeur 5050257
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Incremental Learning for Motion Prediction of Pedestrians and Vehicles
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Incremental Learning for Motion Prediction of Pedestrians and Vehicles
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Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -I: Background.- Probabilistic Models.- II: State of the Art.- Intentional Motion Prediction.- Hidden Markov Models.- III: Proposed Approach.- Growing Hidden Markov Models.- Learning and Predicting Motion with GHMMs.- IV: Experiments.- Experimental Data.- Experimental Results.- V: Conclusion.- Conclusions and Future Work.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 176 pp. Englisch. N° de réf. du vendeur 9783642136412
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Incremental Learning for Motion Prediction of Pedestrians and Vehicles
Edité par
Springer Berlin Heidelberg, 2010
ISBN 10 : 3642136419
ISBN 13 : 9783642136412
Neuf
Couverture rigide
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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Modeling and predicting human and vehicle motion is an active research domain.Owing to the difficulty in modeling the various factors that determine motion(e.g. internal state, perception) this is often tackled by applying machinelearning techniques to build a statistical model, using as input a collectionof trajectories gathered through a sensor (e.g. camera, laser scanner), and thenusing that model to predict further motion. Unfortunately, most currenttechniques use offline learning algorithms, meaning that they are not able tolearn new motion patterns once the learning stage has finished.This books presents a lifelong learning approach where motion patterns can belearned incrementally, and in parallel with prediction. The approach is based ona novel extension to hidden Markov models, and the main contribution presentedin this book, called growing hidden Markov models, which gives us the ability tolearn incrementally both the parameters and the structure of the model. Theproposed approach has been extensively validated with synthetic and realtrajectory data. In our experiments our approach consistently learned motionmodels that were more compact and accurate than those produced by two otherstate-of-the-art techniques, confirming the viability of lifelong learningapproaches to build human behavior models. N° de réf. du vendeur 9783642136412
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Incremental Learning for Motion Prediction of Pedestrians and Vehicles
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Springer Spektrum, 2010
ISBN 10 : 3642136419
ISBN 13 : 9783642136412
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Couverture rigide
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Etat : Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Roboticsis undergoingamajortransformationinscopeanddimension.From a largelydominantindustrialfocus,roboticsis rapidly expandinginto human environments and vigorouslyengaged in its new challenges. Interacting with, assisting, serving, and exploring with humans, the emerging robots will - creasingly touch people and their lives. Beyond its impact on physical robots, the body of knowledge robotics has produced is revealing a much wider range of applications reaching across - verse research areas and scienti?c disciplines, such as: biomechanics, haptics, neurosciences, virtual simulation, animation, surgery, and sensor networks among others. In return, the challenges of the new emerging areas are pr- ing an abundant source of stimulation and insights for the ?eld of robotics. It is indeed at the intersection of disciplines that the most striking advances happen. TheSpringerTractsinAdvancedRobotics(STAR)isdevotedtobringingto the research community the latest advances in the robotics ?eld on the basis of their signi?cance and quality. Through a wide and timely dissemination of critical research developments in robotics, our objective with this series is to promotemoreexchangesandcollaborationsamongtheresearchersinthec- munity and contributeto further advancements inthis rapidlygrowing?eld. The monographwritten byAlejandro DizanVasquez Goveafocusesonthe practicalproblem of moving in a cluttered environment with pedestrians and vehicles. A frameworkbased 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. N° de réf. du vendeur 7770127/12
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