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Object localisation via action recognition

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conference contribution
posted on 2016-02-10, 11:35 authored by John Darby, Baihua LiBaihua Li, Ryan Cunningham, Nicholas Costen
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.



  • Science


  • Computer Science

Published in

Proceedings - International Conference on Pattern Recognition


817 - 820


DARBY, J. ... et al, 2012. Object localisation via action recognition. Proceedings - 21st International Conference on Pattern Recognition, 11th-15th November 2012, Tsukuba, pp.817-820


IEEE (© ICPR2012 Organizing Committee)


AM (Accepted Manuscript)

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