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Lecture Notes in Computer Science
The automatic detection of human activities requires large computational resources to increase recognition performances and sophisticated capturing devices to produce accurate results. Anyway, often innovative analysis methods applied to data extracted by off-the-shelf detection peripherals can return acceptable outcomes. In this paper a framework is proposed for automated posture recognition, exploiting depth data provided by a commercial tracking device. The detection problem is handled as adoi:10.1007/978-3-319-09912-5_14 fatcat:2aumm6q7szbivae4dystphmaja