Abstract
Live video computing (LVC) on distributed smart cameras has many important applications; and a database approach based on a Live Video DataBase Management System (LVDBMS) has shown to be effective for general LVC application development. The performance of such a database system relies on accurate interpretation of spatial relationships among objects in the live video. With the popularity of affordable depth cameras, 3D spatial computation techniques have been applied. However, the 3D object models currently used are expensive to compute, and offer limited scalability. We address this drawback in this article by proposing an octree-based 3D spatial logic and presenting algorithms for computing 3D spatial relationships using depth cameras. To support continuous query processing on live video streams, we also develop a GPU-based implementation of the proposed technique to further enhance scalability for real-time applications. Extensive performance studies based on a public RGB-D dataset as well as the LVDBMS prototype demonstrates the correctness and efficiency of our techniques.
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