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Video System

Tools​

  • VideoFlow: Python framework that facilitates the quick development of complex video analysis applications and other series-processing based applications in a multiprocessing environment. [GitHub]
  • VidGear: Powerful Multi-Threaded OpenCV and FFmpeg based Turbo Video Processing Python Library with unique State-of-the-Art Features. [GitHub]
  • NVIDIA DALI: A library containing both highly optimized building blocks and an execution engine for data pre-processing in deep learning applications [GitHub]
  • TensorStream: A library for real-time video stream decoding to CUDA memory [GitHub]
  • C++ image processing library with using of SIMD: SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, AVX, AVX2, AVX-512, VMX(Altivec) [GitHub]
  • Pretrained image and video models for Pytorch. [GitHub]
  • LiveDetect - Live video client to DeepDetect. [GitHub]

Video Analysis Papers​

  • Server-Driven Video Streaming for Deep Learning Inference [Paper]
    • Kuntai Du, Ahsan Pervaiz, Xin Yuan, Aakanksha Chowdhery, Qizheng Zhang, Henry Hoffmann, Junchen Jiang (SIGCOMM2020)
  • Reducto: On-Camera Filtering for ResourceEfficient Real-Time Video Analytics [Paper]
    • Yuanqi Li, Arthi Padmanabhan, Pengzhan Zhao, Yufei Wang, Guoqing Harry Xu, Ravi Netravali. (SIGCOMM2020)
  • Fu, Daniel Y., et al. "Rekall: Specifying video events using compositions of spatiotemporal labels." arXiv preprint arXiv:1910.02993 (2019). [Paper]
  • Puffer: Puffer is a Stanford University research study about using machine learning to improve video-streaming algorithms. Please visit [GitHub]
  • Visual Road: A Video Data Management Benchmark [Project Website]
    • Brandon Haynes, Amrita Mazumdar, Magdalena Balazinska, Luis Ceze, Alvin Cheung (SIGMOD 2019)
  • CaTDet: Cascaded Tracked Detector for Efficient Object Detection from Video [Paper]
    • Mao, Huizi, Taeyoung Kong, and William J. Dally. (SysML2019)
  • Live Video Analytics at Scale with Approximation and Delay-Tolerance [Paper]
    • Zhang, Haoyu, Ganesh Ananthanarayanan, Peter Bodik, Matthai Philipose, Paramvir Bahl, and Michael J. Freedman. (NSDI 2017)
  • Chameleon: scalable adaptation of video analytics [Paper]
    • Jiang, Junchen, et al. (SIGCOMM 2018)
    • Summary: Configuration controller for balancing accuracy and resource. Golden configuration is a good design. Periodic profiling often exceeded any resource savings gained by adapting the configurations.
  • Kang, Daniel, Peter Bailis, and Matei Zaharia. "Blazeit: Fast exploratory video queries using neural networks." arXiv preprint arXiv:1805.01046 (2018). [Paper]
  • Noscope: optimizing neural network queries over video at scale [Paper] [GitHub]
    • Kang, Daniel, John Emmons, Firas Abuzaid, Peter Bailis, and Matei Zaharia. (VLDB2017)
    • Summary: Information cache + difference detection model + small detection model + sequence optimizer
  • SVE: Distributed video processing at Facebook scale [Paper]
    • Huang, Qi, et al. (SOSP2017)
  • Scanner: Efficient Video Analysis at Scale [Paper][GitHub]
    • Poms, Alex, Will Crichton, Pat Hanrahan, and Kayvon Fatahalian (SIGGRAPH 2018)
  • A cloud-based large-scale distributed video analysis system [Paper]
    • Wang, Yongzhe, et al. (ICIP 2016)
  • Rosetta: Large scale system for text detection and recognition in images [Paper]
    • Borisyuk, Fedor, Albert Gordo, and Viswanath Sivakumar. (KDD 2018)

Video Streaming Papers​

  • Neural-Enhanced Live Streaming: Improving Live Video Ingest via Online Learning [Paper]
    • Jaehong Kim, Youngmok Jung, Hyunho Yeo, Juncheol Ye, and Dongsu Han (SIGCOMM2020)
  • Learning in situ: a randomized experiment in video streaming [Paper]
    • Francis Y. Yan and Hudson Ayers, Stanford University; Chenzhi Zhu, Tsinghua University; Sadjad Fouladi, James Hong, Keyi Zhang, Philip Levis, and Keith Winstein, Stanford University (NSDI2020)
  • CSI: Inferring Mobile ABR Video Adaptation Behavior under HTTPS and QUIC [Paper]
    • Shichang Xu (University of Michigan), Subhabrata Sen (AT&T Labs Research), Z. Morley Mao (University of Michigan) (Eurosys2020)
  • Reconstructing proprietary video streaming algorithms [Paper]
    • Maximilian Grüner, Melissa Licciardello, and Ankit Singla, ETH Zürich (ATC2020)
  • Neural adaptive content-aware internet video delivery. [Paper] [GitHub]
    • Yeo, H., Jung, Y., Kim, J., Shin, J. and Han, D., 2018. (OSDI 2018)
    • Summary: Combine video super-resolution and ABR