Low-Cost and Real-Time Industrial Human Action Recognitions Based on Large-Scale Foundation Models
<|reference_start|>Low-Cost and Real-Time Industrial Human Action Recognitions Based on Large-Scale Foundation Models: 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.<|reference_end|>
@article{liang2024low-cost,
    title={Low-Cost and Real-Time Industrial Human Action Recognitions Based on
  Large-Scale Foundation Models},
    author={Wensheng Liang, Ruiyan Zhuang, Xianwei Shi, Shuai Li, Zhicheng Wang,
  and Xiaoguang Ma},
    journal={arXiv preprint arXiv:2403.08420},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08420},
    primaryClass={cs.CV}
}