BehaveAI case study datasets

BehaveAI is a user-friendly tool that identifies and classifies animals and other objects in video footage from their movement as well as their static appearance. The framework converts motion information into false colours that allow both the human annotator and fully convolutional neural network (F-CNN) to easily identify patterns of movement. The framework integrates both motion streams and static streams in a similar fashion to the mammalian visual system, separating the tasks of detection and classification. The framework also supports hierarchical models (e.g. detect something from it's movement, then work out what exactly it is from conventional static appearance, or vice-versa); and semi-supervised annotation, allowing the annotator to rapidly correct errors made by initial models, making for a more efficient and effective training process.This repository includes data used for the case-studies.