Deep Video Analytics 
by Akshay Bhat
Jan 2020 Update: I have decided to cease public development of this repo, more infromation is present in Archived.md.
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.
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.