Low-Cost and Real-Time Industrial Human Action Recognitions Based on
  Large-Scale Foundation Models
Wensheng Liang Ruiyan Zhuang Xianwei Shi Shuai Li Zhicheng Wang Xiaoguang Ma
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.