SMRT: Surveillance Monitoring and Recognition Techniques for Analyzing Service Behavior in Blurred and Unsteady Video

Abstract

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.