A Vehicle Parking Detection Method Based on Correlation of Magnetic Signals

Hongmei Zhu and Fengqi Yu

International Journal of Distributed Sensor Networks, 2015, vol. 11, issue 7, 361242

Abstract: Recently, significant research efforts have been focused on vehicle parking detection due to fuel consumption and traffic congestion. Many solutions have been successfully applied in indoor parking lots. However, due to the strong noise disturbance in outdoor parking environment, the detection accuracy for on-street parking is still a challenging task. In this paper, we propose a vehicle parking detection method by the use of normalized cross-correlation (NCC) of magnetic signals generated by magnetoresistive sensors. In the proposed method, the sensed signal is correlated with a reference. If the result is greater than a threshold, a pulse is generated. One of the primary factors that affect the accuracy of the NCC-based detection is the choice of reference which is obtained by using a k -means clustering algorithm in this paper. Compared with the-state-of-the-art vehicle detection methods, the proposed method is competitive in terms of cost, accuracy, and complexity. The proposed method is simulated and tested on the Xueyuan Boulevard, University Town of Shenzhen, Nanshan, Shenzhen, China. The experimental results show that the proposed method can provide the detection accuracy of 99.33% for arrival and 99.63% for departure.

Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1155/2015/361242 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:11:y:2015:i:7:p:361242

DOI: 10.1155/2015/361242

Access Statistics for this article

More articles in International Journal of Distributed Sensor Networks
Bibliographic data for series maintained by SAGE Publications ().