A realtime algorithm for human action recognition in RGB and thermal video

Monitoring the movement and actions of humans in video in realtime is an important task. We present a deep learning based algorithm for human action recognition for both RGB and thermal cameras. It is able to detect and track humans and recognize four basic actions (standing, walking, running, lying) in realtime on a notebook with a NVIDIA GPU. For this, it combines state of the art components for object detection (ScaledYoloV4), optical flow (RAFT) and pose estimation (EvoSkeleton). Qualitative experiments on a set of tunnel videos show that the proposed algorithm works robustly for both RGB and thermal video.