Deep Video Analytics    

by Akshay Bhat

Deep Video Analytics is a platform for indexing and extracting information from videos and images. With latest version of docker installed correctly, you can run Deep Video Analytics in minutes locally (even without a GPU) using a single command.

May 2019 Update: Over the last few months I have been developing DVA on a private repo and will be updating the public version in Fall 2019.

Installation & Overview

For installation instructions and overview please visit https://www.deepvideoanalytics.com and go through the presentation. The standalone OCR example has been moved to /docs/experiments/ocr directory.

Google Cloud Platform

Deep Video Analytics recommends Google Cloud Platform and is configured to work out of box.

Architecture

Deep Video Analytics implements a client-server architecture pattern, where clients can access state of the server via a REST API. For uploading, processing data, training models, performing queries, i.e. mutating the state clients can send DVAPQL (Deep Video Analytics Processing and Query Language) formatted as JSON. Each query represents a directed acyclic graph of operations.

Libraries present in this repository 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
Insight Face	MIT License
CRNN.pytorch	MIT License
Original CRNN code by Baoguang Shi	MIT License
Object Detector App using TF Object detection API	MIT License
Plotly.js	MIT License
Text Detection CTPN	MIT License
SphereFace	MIT License
Segment annotator	BSD 3-clause
Youtube 8M feature extractor weights	Apache 2.0
LOPQ	Apache 2.0
Open Images Pre-trained network	Apache 2.0
Interval Tree	Apache 2.0

Libraries present in container (/root/thirdparty/)

Library	Link to the license
faiss	BSD + PATENTS License
dlib	Boost Software License

Additional libraries & frameworks

• FFmpeg (not linked, called via a Subprocess)
• Tensorflow
• OpenCV
• Numpy
• Pytorch
• Docker
• LMDB
• Nvidia-docker
• Docker-compose
• All packages in requirements.txt
• All dependancies installed in CPU Dockerfile & GPU Dockerfile

License & Copyright

Copyright 2016-2018, Akshay Bhat, All rights reserved.

Contact

Please contact me for more information.