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Deep Video Analytics     Build Status Build Status

by Akshay Bhat

Jan 2020 Update: I have decided to cease public development of this repo, more infromation is present in

UI Screenshot

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 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.

Open in Cloud Shell


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
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

License & Copyright

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


Please contact me for more information.