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- W2183753118 abstract "In this paper we present a system suitable for real-time human tracking and predefined human gestures detection using depth data acquired from Kinect sensor installed right above the detection region. The tracking part is based on fitting an articulated human body model to obtained data using particle filter framework and specifically defined constraints which originate in physiological properties of the human body. The gesture recognition part utilizes the timed automaton conforming to the human body poses and regarding tolerances of the joints positions and time constraints. The system was tested against the manually annotated 61-minutes-long recording of the depth data where ten different people were tracked. The 92.38% sensitivity was reached as well as the real-time performance exceeding 30 FPS. No a priori knowledge about the tracked person is required which makes the system suitable for seamless human-computer interaction solutions, security applications or entertainment industry where the position of sensors must not interfere with the detection region." @default.
- W2183753118 created "2016-06-24" @default.
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- W2183753118 date "2015-01-01" @default.
- W2183753118 modified "2023-09-27" @default.
- W2183753118 title "Human gesture recognition using top view depth data obtained from Kinect sensor" @default.
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