Published in Data Science and Engineering (DSE) Record 2025 Vol. 6 No. 1 pp. 91-130
Abstract
This research presents a comprehensive framework for analyzing customer behavior in walking street markets using advanced person re-identification techniques. We deployed dual CCTV cameras at strategic points along a 200-meter section of a walking street market in Chiang Mai, Thailand, to track customer movements and analyze behavioral patterns. Our methodol-ogy comprises three main components: (1) a novel segmentation-enhanced multi-region feature extraction framework combining YOLOv11 segmenta-tion with Swin Transformer, (2) a robust person re-identification approach with PCA-enhanced feature matching, and (3) detailed customer behavior analysis based on movement patterns, speeds, and interactions. Our feature extraction method achieves 92.31% Rank-1 accuracy and 59.62% mAP, significantly outperforming traditional approaches. Using the re-identification results, we identify five distinct customer behavior types (Goal-Oriented, Browsing, Lingering, Focused, and Brief Visitors) with ac-tionable insights for market management. This research contributes both methodological advances in per-son re-identification and practical applica-tions for retail analytics in dynamic public spaces.