| Title |
An integrated vision–mobile fusion framework for real-time smart parking navigation |
| Authors |
Laptiev, Oleksandr ; Thuruthel Murali, Ananthakrishnan ; Saab, Nathalie ; Soltanov, Nihad ; Paulauskaitė-Tarasevičienė, Agnė |
| DOI |
10.3390/logistics10040084 |
| Full Text |
|
| Is Part of |
Logistics.. Basel : MDPI. 2026, vol. 10, iss. 4, art. no. 84, p. 1-22.. ISSN 2305-6290 |
| Keywords [eng] |
homography ; indoor navigation ; mobile data fusion ; parking navigation ; smart parking ; vehicle detection |
| Abstract [eng] |
Background: Efficient parking navigation in large and dynamic parking areas requires systems that can adapt to real-time conditions and provide precise vehicle localization. Methods: This paper presents a smart car parking navigation module that integrates camera-based vehicle perception, homography-based ground-plane localization, mobile GNSS positioning, and dynamic route planning into a unified framework. Instance segmentation (YOLOv8n-seg) is used to detect vehicles and extract ground-contact regions, which are associated with parking slots defined in a GeoJSON-based site model. Mobile GNSS data are fused with visual observations via spatio-temporal proximity scoring to enable robust user–vehicle matching without optical identification. An A* routing algorithm dynamically computes and updates navigation paths, adapting to lane obstructions and slot availability in real time. Results: Experimental evaluation on a real six-camera parking facility shows that the proposed segmentation-based localization reduces mean error from 0.732 m to 0.283 m (61.3% improvement), with the 95th-percentile error dropping from 1.892 m to 0.908 m, and outperforming the bounding-box baseline in 85.3% of detections. Conclusions: These results demonstrate that sub-meter vehicle localization and reliable user–vehicle association are achievable using standard surveillance cameras without specialized infrastructure, offering a scalable and cost-effective solution for intelligent parking navigation. |
| Published |
Basel : MDPI |
| Type |
Journal article |
| Language |
English |
| Publication date |
2026 |
| CC license |
|
