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008 100715s2010 gw | s |||| 0|eng d
020 _a9783642136429
_9978-3-642-13642-9
040 _cMX-MeUAM
050 4 _aTJ210.2-211.495
050 4 _aT59.5
082 0 4 _a629.892
_223
100 1 _aGovea, Alejandro Dizan Vasquez.
_eauthor.
245 1 0 _aIncremental Learning for Motion Prediction of Pedestrians and Vehicles
_h[recurso electrónico] /
_cby Alejandro Dizan Vasquez Govea.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2010.
300 _a160p. 35 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Tracts in Advanced Robotics,
_x1610-7438 ;
_v64
505 0 _aI: 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.
520 _aModeling 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 machine learning techniques to build a statistical model, using as input a collection of trajectories gathered through a sensor (e.g. camera, laser scanner), and then using that model to predict further motion. Unfortunately, most current techniques use offline learning algorithms, meaning that they are not able to learn new motion patterns once the learning stage has finished. This books presents a lifelong learning approach where motion patterns can be learned incrementally, and in parallel with prediction. The approach is based on a novel extension to hidden Markov models, and the main contribution presented in this book, called growing hidden Markov models, which gives us the ability to learn incrementally both the parameters and the structure of the model. The proposed approach has been extensively validated with synthetic and real trajectory data. In our experiments our approach consistently learned motion models that were more compact and accurate than those produced by two other state-of-the-art techniques, confirming the viability of lifelong learning approaches to build human behavior models.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aOptical pattern recognition.
650 1 4 _aEngineering.
650 2 4 _aRobotics and Automation.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aPattern Recognition.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642136412
830 0 _aSpringer Tracts in Advanced Robotics,
_x1610-7438 ;
_v64
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-13642-9
596 _a19
942 _cLIBRO_ELEC
999 _c202429
_d202429