Enhanced Indoor Scene Recognition and Localization for Pedestrian Using Minimum Matching Group of Pedestrian Steps

Abstract

Indoor positioning acts as a fundamental infrastructure for Internet of Things (IoT) and artificial intelligence systems. However, conventional landmark-based positioning methods suffer from degraded performance in complex indoor scenarios, where complicated path layouts and varied pedestrian behaviors hinder reliable scene recognition. This work presents an integrated framework for indoor scene recognition and pedestrian localization. The core module adopts the minimum matching group of pedestrian steps (MMG-PS) to detect scene transitions, and a machine learning model is deployed to fuse real-time sensor data and historical trajectory patterns for enhanced recognition accuracy. To adapt to different indoor environments, two dedicated access point (AP) selection strategies are developed. Principal component analysis (PCA) is applied to corridor-like linear spaces, while the eight-diagram (ED) algorithm is customized for open halls. The proposed system achieves pedestrian positioning and scene transition detection simultaneously. Extensive experiments conducted in an office complex and a shopping mall demonstrate that our method yields superior performance compared with traditional approaches.