Monitoring the movement and actions of humans in video in real-time 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 real-time on a notebook with a NVIDIA GPU. For this, it combines state of the art components for object detection (Scaled-YoloV4), 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.

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Hannes Fassold, Karlheinz Gutjahr, Anna Weber, and Roland Perko, "A real-time algorithm for human action recognition in RGB and thermal video," Proc. SPIE 12528, Real-Time Image Processing and Deep Learning 2023, 1252804 (Presented at SPIE Defense + Commercial Sensing: May 01, 2023; Published: 13 June 2023); https://doi.org/10.1117/12.2657033.