Low-Cost CCTV Repurposing for Sustainable Parking Management: A Non-AI Computer Vision Case Study

Namya Kamboj; Kongwen Zhang

doi:10.62177/jaet.v2i4.666

Authors

Namya Kamboj University of the Fraser Valley
Kongwen Zhang University of the Fraser Valley https://orcid.org/0000-0001-7570-9805

DOI:

https://doi.org/10.62177/jaet.v2i4.666

Keywords:

Parking Occupancy Monitoring, Low-Cost Computer Vision, Adaptive Thresholding, Sustainable Urban Mobility, Polygon Masking, Non-AI Solution

Abstract

Urban parking inefficiency results in fuel waste and emissions, yet the current monitoring systems rely on resource-intensive AI or sensor-based approaches. Aligning with a focus on sustainable technology, this project demonstrates how deterministic computer-vision techniques (using adaptive thresholding and polygon masking) can transform the existing CCTV camera framework into parking occupancy detectors without AI. This system deploys an open-source pipeline, combining OpenCV and PyQt5, on UFV’s infrastructure, which requires zero hardware costs and consumes 95% less power than other GPU-based solutions. Testing with 18,000+ frames of simulated CCTV footage, the system achieved approximately 99% accuracy. This case study presents a replicable solution for institutions in resource-constrained environments, demonstrating that an economical IoT-CV integration can optimize urban resources while minimizing AI’s carbon footprint.

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Author Biography

Kongwen Zhang, University of the Fraser Valley

Frank Zhang is an associate professor in the School of Computing at the University of the Fraser Valley (UFV), Abbotsford, Canada.

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