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Conference Paper
Vehicle Map Mapping and Parking Occupancy Estimation System with No Ambiguity
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- Authors
- Cheonin Oh, Sungwoong Shin, Daesub Yoon, Sunglok Choi
- Issue Date
- 2023-10
- Citation
- International Conference on Systems, Man, and Cybernetics (SMC) 2023, pp.1518-1523
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- This paper presents a novel approach for vehicle map mapping and estimating parking occupancy in parking lots using a single camera without parking lines. The system is capable of detecting around 10 vehicles even with just one camera, and is able to track vehicles with the same ID as they enter and pass through the parking lot. Furthermore, it proposes a method to map the locations of vehicles on real-world maps, allowing for accurate estimation of parking occupancy. The system achieved high accuracy in vehicle detection and tracking, with a 98.4% recall rate and 100% tracking accuracy. The accuracy of parking occupancy estimation was calculated using root mean square error, with an average error of 0.24m and a maximum error of 0.36m. The results demonstrate the feasibility of detecting and tracking vehicles, as well as estimating parking occupancy, in parking lots without parking lines using only one camera. Further testing on multiple sites, including nighttime and adverse weather conditions, is needed to increase the reliability of the system.
- KSP Keywords
- Adverse Weather Conditions, Average error, High accuracy, Maximum error, Novel approach, Parking lines, Parking lot, Real-world, Recall rate, Root mean square(RMS), Vehicle detection and tracking