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Jointly Learning Visual Poses and Pose Lexicon for Semantic Action Recognition
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In: Faculty of Engineering and Information Sciences - Papers: Part B (2019)
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Abstract:
A novel method for semantic action recognition through learning a pose lexicon is presented in this paper. A pose lexicon comprises a set of semantic poses, a set of visual poses and a probabilistic mapping between visual and semantic poses. This paper assumes that both visual poses and mapping are hidden and proposes a method to simultaneously learn a visual pose model that estimates the likelihood of an observed video frame being generated from hidden visual poses, and a pose lexicon model that establishes the probabilistic mapping between the hidden visual poses and the semantic poses parsed from textual instructions. Specifically, the proposed method consists of two-level hidden Markov models. One level represents the alignment between the visual poses and semantic poses. The other level represents a visual pose sequence and each visual pose is modelled as a Gaussian mixture. An Expectation-maximization algorithm is developed to train a pose lexicon. With the learned lexicon, action classification is formulated as a problem of finding the maximum posterior probability of a given sequence of video frames that follows a given sequence of semantic poses, constrained by the most likely visual pose and the alignment sequences. The proposed method was evaluated on MSRC- 12, WorkoutSU-10, WorkoutUOW-18, Combined-15, Combined- 17 and Combined-50 action datasets using cross-subject, crossdataset, zero-shot and seen/unseen protocols.
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Keyword:
Engineering; Science and Technology Studies
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URL: https://ro.uow.edu.au/eispapers1/2283
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| 2 |
Detection of ground parrot vocalisation: a multiple instance learning approach
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| 3 |
Semantic action recognition by learning a pose lexicon
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In: Faculty of Engineering and Information Sciences - Papers: Part B (2017)
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| 4 |
Learning a pose lexicon for semantic action recognition
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In: Faculty of Engineering and Information Sciences - Papers: Part A (2016)
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| 5 |
Measuring face familiarity and its application to face recognition
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In: Faculty of Engineering and Information Sciences - Papers: Part A (2012)
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| 6 |
Finding distinctive facial areas for face recognition
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In: Faculty of Informatics - Papers (Archive) (2010)
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