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.
@article{arxiv.2304.01567,
title = {A real-time algorithm for human action recognition in RGB and thermal video},
author = {Hannes Fassold and Karlheinz Gutjahr and Anna Weber and Roland Perko},
journal= {arXiv preprint arXiv:2304.01567},
year = {2023}
}
Comments
Accepted for SPIE Real-Time Image Processing and Deep Learning Conference 2023