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A highly configurable visual search & analytics platform for images and videos.

Home Page: https://www.deepvideoanalytics.com/

Shell 0.01% Nginx 0.01% Python 45.25% CSS 2.86% HTML 13.64% PHP 0.03% Lua 0.01% Jupyter Notebook 38.20% Protocol Buffer 0.01%

deepvideoanalytics's Introduction

Deep Video Analytics • Build Status

UI Screenshot Banner

Don't be worried by complexity of this banner, with latest version of docker installed correctly, you can run Deep Video Analytics in minutes locally (even without a GPU) using a single command.

Data & processing model

Data model

Deep Video Analytics is a platform for indexing and extracting information from videos and images. For installation instructions & demo go to https://www.deepvideoanalytics.com

Libraries used/modified in code and their licenses

Library Link to the license
YAD2K MIT License
AdminLTE2 MIT License
FabricJS MIT License
Facenet MIT License
JSFeat MIT License
MTCNN MIT License
CRNN.pytorch MIT License
Original CRNN code by Baoguang Shi MIT License
Modified PySceneDetect BSD 2-Clause
Segment annotator BSD 3-clause
Modified SSD-Tensorflow Apache 2.0 License (Individual files)
LOPQ Apache 2.0 License
Open Images Pre-trained network Apache 2.0 License

Additionally following libraries & frameworks are installed when building/running the container

  • FFmpeg (not linked, called via a Subprocess)
  • Tensorflow
  • OpenCV
  • Numpy
  • Pytorch
  • Docker
  • Nvidia-docker
  • Docker-compose
  • All packages in requirements.txt & used in Dockerfiles.

License & Copyright

Copyright 2016-2017, Akshay Bhat, Cornell University, All rights reserved.

Contact

Deep Video Analytics is currently in active development. The license will be relaxed once the a stable release version is reached. Please contact me for more information. For more information see answer on this issue

deepvideoanalytics's People

Contributors

akshaybhatai avatar

Watchers

James Cloos avatar  avatar

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