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The accurate localization of persons or objects, both indoors and out of doors, is an interesting scientific challenge with numerous practical applications [1].
The second most frequent type of data input is localization of persons (31%).
The systems are classified by five types of data gathering or data input: monitoring of persons or objects; localization of persons or objects as well as manual input or request by the user.
Monitoring of persons is by far the most important type of data gathering for analytical and diagnostic support (48%), alerting (36%) as well as support activities (19%), followed by localization of persons.
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We have introduced a novel spatiotemporal deformable part model for the localization of two-person interactions.
The main advantage of this approach is that accurate localization of a person is not required.
We focus on the localization of two person interactions that are characterized by the coordination of the movements and poses of both individuals.
False positive localization of a handshake when two persons are just about to hug (score 0.67) (right).
False positive localization of a hug with two persons about to cross each other after a distractor interaction (score 0.36) (right).
The item-person map displays the localization of the item parameters, as well as the distribution of the person parameters, along the latent trait.
We introduce a novel spatiotemporal deformable part model for the localization of fine-grained human interactions of two persons in unsegmented videos.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com