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- W2561001488 abstract "Animal Behavioural Monitoring requires near real-time and robust scene information. State of art methods to construct a complete RGB-D video of a scene are typically slow, or done offline and often both computationally and monetarily expensive. In this paper, we present a near real-time method called KinectScenes that fuses N RGB-D point clouds acquired from statically positioned commodity hardware to reconstruct large a complete dynamic 3D scenes. Specifically, we introduce a pipeline that fuses RGB-D sensor data to generate a spatio-temporal coherent 3D free viewpoint video with near 360° purview -- only limited by the number and placement of the sensors. KinectScenes differentiates from existing state of art in that 1. It is near real-time: 7~15 fps, and 2. It remains unperturbed to minute motion of the static sensors. In addition, we contribute a new dataset of RGB-D scenes to the research community as to enable rapid progress for behavioral tracking research. Applications include precise human and animal behavioral tracking, inexpensive VR scene generation, object recognition and interaction. Early results demonstrate that KinectScenes is both efficient in near real-time and can densely and richly reconstruct a given dynamic scene using 3 sensors." @default.
- W2561001488 created "2017-01-06" @default.
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- W2561001488 date "2016-06-01" @default.
- W2561001488 modified "2023-09-25" @default.
- W2561001488 title "KinectScenes: Robust Real-Time RGB-D Fusion for Animal Behavioural Monitoring" @default.
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- W2561001488 doi "https://doi.org/10.1109/crv.2016.70" @default.
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