Realtime-capable Human Activity Recognition

Implemented a realtime video analysis pipeline for Human Activity Recognition (HAR) using deep learning models. A major challenge was the complexity of modern deep learning approaches for HAR, which were not realtime capable. Several improvements have been made to SlowFast and PoseC3D (two state of the art models at that time) to optimize computations and speed up inference time. Concrete project details under NDA.