BU-CVKit: Extendable Computer Vision Framework for Species Independent
  Tracking and Analysis
Mahir Patel Lucas Carstensen Yiwen Gu Michael E. Hasselmo Margrit Betke
A major bottleneck of interdisciplinary computer vision (CV) research is the
lack of a framework that eases the reuse and abstraction of state-of-the-art CV
models by CV and non-CV researchers alike. We present here BU-CVKit, a computer
vision framework that allows the creation of research pipelines with chainable
Processors. The community can create plugins of their work for the framework,
hence improving the re-usability, accessibility, and exposure of their work
with minimal overhead. Furthermore, we provide MuSeqPose Kit, a user interface
for the pose estimation package of BU-CVKit, which automatically scans for
installed plugins and programmatically generates an interface for them based on
the metadata provided by the user. It also provides software support for
standard pose estimation features such as annotations, 3D reconstruction,
reprojection, and camera calibration. Finally, we show examples of behavioral
neuroscience pipelines created through the sample plugins created for our
framework.