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Low-Cost and Real-Time Industrial Human Action Recognitions Based on Large-Scale Foundation Models
March 14, 2024, 4:46 a.m. | Wensheng Liang, Ruiyan Zhuang, Xianwei Shi, Shuai Li, Zhicheng Wang, Xiaoguang Ma
cs.CV updates on arXiv.org arxiv.org
Abstract: 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 …
abstract arxiv control cost costs cs.cv etc foundation human industrial low optimization paper performance quality real-time safety scale type
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