Learning motion patterns in dynamic environments is a key component of any context-aware robotic system, and probabilistic mixture models provide a sound framework for mining these patterns. This paper presents an approach for learning motion models from trajectories provided by the tracking system of a moving platform. We present a learning approach in which a Linear Dynamical System (LDS) is augmented with a discrete hidden variable that has a number of states equal to the number of behaviours in the environment. As a result, a mixture of linear dynamical systems (MLDSs) capable of explaining several motion behaviours is developed. The model is learned by means of the Expectation Maximization (EM) algorithm.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Unsupervised motion learning from a moving platform


    Contributors:


    Publication date :

    2013-06-01


    Size :

    414953 byte



    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Unsupervised motion learning from a moving platform

    Romero-Cano, Victor / Nieto, Juan I. / Agamennoni, Gabriel | IEEE | 2013


    UNSUPERVISED MOTION LEARNING FROM A MOVING PLATFORM

    Romero-Cano, V. / Nieto, J. / Agamennoni, G. et al. | British Library Conference Proceedings | 2013


    Unsupervised flow-based motion analysis for an autonomous moving system

    Pinto, A. M. / Correia, M. V. / Paulo Moreira, A. et al. | British Library Online Contents | 2014


    An Unsupervised, Online Learning Framework for Moving Object Detection

    Nair, V. / Clark, J. / IEEE Computer Society | British Library Conference Proceedings | 2004


    STEREO-BASED MOTION DETECTION AND TRACKING FROM A MOVING PLATFORM

    Romero-Cano, V. / Nieto, J. / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2013