  Industrial managements, including quality control, cost and safety
optimization, etc., heavily rely on high quality industrial human action
recognitions (IHARs) which were hard to be implemented in large-scale
industrial scenes due to their high costs and poor real-time performance. In
this paper, we proposed a large-scale foundation model(LSFM)-based IHAR method,
wherein various LSFMs and lightweight methods were jointly used, for the first
time, to fulfill low-cost dataset establishment and real-time IHARs.
Comprehensive tests on in-situ large-scale industrial manufacturing lines
elucidated that the proposed method realized great reduction on employment
costs, superior real-time performance, and satisfactory accuracy and
generalization capabilities, indicating its great potential as a backbone IHAR
method, especially for large-scale industrial applications.
