Deep Video Analytics •

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Data & processing model

Author: Akshay Bhat, Cornell University.

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	Directory
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
Object Detector App using TF Object detection API	MIT License	repos/tfdetection/
Modified PySceneDetect	BSD 2-Clause
Segment annotator	BSD 3-clause
Modified SSD-Tensorflow	Apache 2.0 License (Individual files)
LOPQ	Apache 2.0 License	repos/lopq/
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