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OpenEM is a library that provides advanced video analytics for fisheries electronic monitoring (EM) data. It currently supports detection, classification, counting and measurement of fish during landing or discard.
This functionality is available via a deployment library with pretrained models available in our example data (see tutorial). The base library is written in C++, with bindings available for both Python and C#. Examples are included for all three languages.

The current release also includes a training library for all OpenEM functionality. The library is distributed as a native Windows library and as a Docker image.

Documentation is hosted at read the docs.

Click the image below to see a video of OpenEM in action:

OpenEM has been used in multiple EM projects. We provide in depth case studies and tutorials showing the power and flexibility of the library.

EDF West Coast Activity Recognition

For this project, CVision AI developed an algorithm using data provided by EDF to identify segments of video with specific activities of interest. You can read more about the overall project here. You can follow the tutorial located here

Get the latest Windows library from our GitHub releases page.

The docker image can be obtained with:

docker pull cvisionai/openem:latest