TY  - EJOU
AU  - Liu, Changyu 
AU  - Huang, Hao 
AU  - Huang, Guogang 
AU  - Wu, Chunyin 
AU  - Liang, Yingqi 

TI  - Abnormal Action Detection Based on Parameter-Efficient Transfer Learning in Laboratory Scenarios
T2  - Computers, Materials \& Continua

PY  - 2024
VL  - 80
IS  - 3
SN  - 1546-2226

AB  - Laboratory safety is a critical area of broad societal concern, particularly in the detection of abnormal actions. To enhance the efficiency and accuracy of detecting such actions, this paper introduces a novel method called TubeRAPT (Tubelet Transformer based on Adapter and Prefix Training Module). This method primarily comprises three key components: the TubeR network, an adaptive clustering attention mechanism, and a prefix training module. These components work in synergy to address the challenge of knowledge preservation in models pre-trained on large datasets while maintaining training efficiency. The TubeR network serves as the backbone for spatio-temporal feature extraction, while the adaptive clustering attention mechanism refines the focus on relevant information. The prefix training module facilitates efficient fine-tuning and knowledge transfer. Experimental results demonstrate the effectiveness of TubeRAPT, achieving a 68.44% mean Average Precision (mAP) on the CLA (Crazy Lab Activity) small-scale dataset, marking a significant improvement of 1.53% over the previous TubeR method. This research not only showcases the potential applications of TubeRAPT in the field of abnormal action detection but also offers innovative ideas and technical support for the future development of laboratory safety monitoring technologies. The proposed method has implications for improving safety management systems in various laboratory environments, potentially reducing accidents and enhancing overall workplace safety.
KW  - Parameter-efficient transfer learning; laboratory scenarios; TubeRAPT; abnormal action detection

DO  - 10.32604/cmc.2024.053625