SMRT: Surveillance Monitoring and Recognition Techniques for Analyzing Service Behavior in Blurred and Unsteady Video
學年 113
學期 2
出版（發表）日期 2025-05-15
作品名稱 SMRT: Surveillance Monitoring and Recognition Techniques for Analyzing Service Behavior in Blurred and Unsteady Video
作品名稱（其他語言）
著者 Qiaoyun Zhang,Shih-Yang Yang,Yu Lin,Huan-Chao Keh,Diptendu Sinha Roy,
單位
出版者
著錄名稱、卷期、頁數 vol. 26, no 3, pp. 347-355
摘要 With the rapid development of the service industry and increasing customer expectations, traditional mystery shopper audit methods face several challenges, such as time-consuming manual analysis, significant subjective bias, and difficulty in accurately quantifying complex service behaviors. To overcome these limitations, this paper introduces an innovative approach called Surveillance Monitoring and Recognition Techniques (SMRT) for analyzing service behavior. The proposed SMRT achieves precise classification of service behaviors through a two-phase process: coarse-grained and fine-grained analysis. In the coarse-grained phase, the proposed SMRT preprocesses blurred video to extract and emphasize relevant external features, specifically detecting and capturing ‘person’ objects in video frames, thereby effectively filtering out irrelevant frames and reducing computational load. In the fine-grained phase, it performs spatiotemporal feature extraction and utilizes Transformer models to conduct a detailed comparison of target behavioral features across video segments. Simulation results demonstrate that the proposed SMRT significantly enhances recognition performance in terms of accuracy, and F1-score compared to existing methods.
關鍵字 Blurred and unsteady video, Mystery shopper audit, Service behavior recognition
語言 en
ISSN 1607-9264
期刊性質 國內
收錄於
產學合作
通訊作者
審稿制度 0
國別 TWN
公開徵稿
出版型式 ,電子版