%0 Conference Paper %A Darby, John %A Li, Baihua %A Cunningham, Ryan %A Costen, Nicholas %D 2016 %T Object localisation via action recognition %U https://repository.lboro.ac.uk/articles/conference_contribution/Object_localisation_via_action_recognition/9404411 %2 https://repository.lboro.ac.uk/ndownloader/files/17021105 %K untagged %K Information and Computing Sciences not elsewhere classified %X The aim of this paper is to track objects during their use by humans. The task is difficult because these objects are small, fast-moving and often occluded by the user. We present a novel solution based on cascade action recognition, a learned mapping between body-and object-poses, and a hierarchical extension of importance sampling. During tracking, body pose estimates from a Kinect sensor are classified between action classes by a Support Vector Machine and converted to discriminative object pose hypotheses using a {body, object} pose mapping. They are then mixed with generative hypotheses by the importance sampler and evaluated against the image. The approach out-performs a state of the art adaptive tracker for localisation of 14/15 test implements and additionally gives object classifications and 3D object pose estimates. %I Loughborough University