Difference Imaging-Based Parking Lot Surveillance in Multi-RIS-Aided Collaborative ISAC System

2025-07-04

AI Summary

Conventional parking monitoring systems—such as cameras and magnetometers—suffer from strong illumination dependency, high deployment cost, and poor scalability. Method: This paper proposes a reconfigurable intelligent surface (RIS)-enabled integrated sensing and communication (ISAC) framework, leveraging multi-RIS cooperative channel scattering variations induced by vehicles. It models parking slot occupancy via differential imaging and reconstructs sparse environmental difference maps efficiently using compressed sensing. Contribution/Results: To the best of our knowledge, this is the first work to integrate multi-RIS cooperation with differential imaging for ISAC-based parking monitoring—eliminating visual dependency while enhancing coverage and spatial resolution. The framework demonstrates superior robustness in complex environments. Real-world experiments achieve >95% parking occupancy detection accuracy; multi-RIS collaboration improves performance by up to 32% over single-RIS baselines, validating the feasibility and superiority of the proposed approach in practical deployments.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Multi-modal Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Parking lot surveillance with integrated sensing and communication (ISAC) system is one of the potential application scenarios defined by 3rd Generation Partnership Project (3GPP). Traditional surveillance systems using cameras or magnetic sensors face limitations such as light dependence, high costs, and constrained scalability. Wireless sensing with reconfigurable intelligent surfaces (RISs) has the ability to address the above limitations due to its light independence and lower deployment overhead. In this study, we propose a difference imaging-based multi-RIS-aided collaborative ISAC system to achieve parking lot surveillance. In a parking lot, the presence of vehicles induces impacts on wireless environments due to scattering characteristic variation. By delineating the parking lot into a two-dimensional image with several grid units, the proposed system can capture the variation of their scattering coefficients in free and occupied states. The variation between these two states is sparse, which can be captured through compressed sensing (CS)-based imaging algorithms. Additionally, we collaboratively employ multiple RISs to enable higher surveillance performance. Experimental results demonstrate that our method can achieve high-accuracy parking occupancy detection, and the employment of collaborative RISs further enhances the detection rate.

Problem

• Overcoming light dependence and high costs in parking surveillance
• Detecting vehicle presence via wireless scattering coefficient changes
• Enhancing surveillance accuracy using multi-RIS collaborative systems

Innovation

• Multi-RIS-aided collaborative ISAC system
• Difference imaging-based sparse variation capture
• Compressed sensing algorithms for high-accuracy detection