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  • 1.
    Kjellström, Hedvig
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Romero, Javier
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Martinez, David
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Simultaneous Visual Recognition of Manipulation Actions and Manipulated Objects2008In: Computer Vision - Eccv 2008, Pt Ii, Proceedings / [ed] Forsyth, D; Torr, P; Zisserman, A, 2008, Vol. 5303, p. 336-349Conference paper (Refereed)
    Abstract [en]

    The visual analysis of human manipulation actions is of interest for e.g. human-robot interaction applications where a robot learns how to perform a task by watching a human. In this paper, a method for classifying manipulation actions in the context of the objects manipulated, and classifying objects in the context of the actions used to manipulate them is presented. Hand and object features are extracted from the video sequence using a segmentation based approach. A shape based representation is used for both the hand and the object. Experiments show this representation suitable for representing generic shape classes. The action-object correlation over time is then modeled using conditional random fields. Experimental comparison show great improvement in classification rate when the action-object correlation is taken into account, compared to separate classification of manipulation actions and manipulated objects.

  • 2.
    Martinez, David
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Modeling and recognition of actions through motor primitives2008In: 2008 IEEE International Conference On Robotics And Automation: Vols 1-9, 2008, p. 1704-1709Conference paper (Refereed)
    Abstract [en]

    We investigate modeling and recognition of object manipulation actions for the purpose of imitation based learning in robotics. To model the process, we are using a combination of discriminative (support vector machines, conditional random fields) and generative approaches (hidden Markov models). We examine the hypothesis that complex actions can be represented as a sequence of motion or action primitives. The experimental evaluation, performed with five object manipulation actions and 10 people, investigates the modeling approach of the primitive action structure and compares the performance of the considered generative and discriminative models.

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