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Towards Model Checking Video Streams Using VoxLogicA on GPUs

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13268))

Abstract

We present a feasibility study on the use of spatial logic model checking for real-time analysis of high-resolution video streams with the tool VoxLogicA. VoxLogicA is a voxel-based image analyser based on the Spatial Logic for Closure Spaces, a logic catered to deal with properties of spatial structures such as topological spaces, graphs and polyhedra. The underlying language includes operators to model proximity and reachability. We demonstrate, via the analysis of a series of video frames from a well-known video game, that it is possible to analyse high-resolution videos in real-time by exploiting the speed-up of VoxLogicA-GPU, a recently developed GPU-based version of the tool, which is 1–2 orders of magnitude faster than its previous iteration. Potential applications of real-time video analysis include medical imaging applications such as ultrasound exams, and other video-based diagnostic techniques. More broadly speaking, this work can be the first step towards novel information retrieval methods suitable to find information in a declarative way, in possibly large collections of video streams.

Research partially supported by the MIUR Project PRIN 2017FTXR7S IT-MaTTerS”. The authors are listed in alphabetical order, as they equally contributed to this work.

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Notes

  1. 1.

    VoxLogicA is Free and Open Source Software. Source code and binaries are available at https://github.com/vincenzoml/VoxLogicA.

  2. 2.

    PAC-MAN\(^\textrm{TM}\) & ©1980 BANDAI NAMCO Entertainment Inc.

  3. 3.

    FSharp: see https://fsharp.org. NET Core: see https://dotnet.microsoft.om. OpenCL: see https://www.khronos.org/opencl. ITK: see https://itk.org.

  4. 4.

    See: https://opencv.org/.

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Correspondence to Laura Bussi .

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Bussi, L., Ciancia, V., Gadducci, F., Latella, D., Massink, M. (2022). Towards Model Checking Video Streams Using VoxLogicA on GPUs. In: Bowles, J., Broccia, G., Pellungrini, R. (eds) From Data to Models and Back. DataMod 2021. Lecture Notes in Computer Science, vol 13268. Springer, Cham. https://doi.org/10.1007/978-3-031-16011-0_6

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  • DOI: https://doi.org/10.1007/978-3-031-16011-0_6

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