A real-time algorithm for human action recognition in RGB and thermal video
This addresses monitoring human actions in video for applications like surveillance, but it is incremental as it uses established methods on new data.
The paper tackled real-time human action recognition in RGB and thermal video by combining existing deep learning components, achieving robust performance in qualitative experiments on tunnel videos.
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.