• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Fast, Accurate Vehicle Detection and Distance Estimation

Vol. 14, No. 2, February 29, 2020
10.3837/tiis.2020.02.008, Download Paper (Free):

Abstract

A large number of people suffered from traffic accidents each year, so people pay more attention to traffic safety. However, the traditional methods use laser sensors to calculate the vehicle distance at a very high cost. In this paper, we propose a method based on deep learning to calculate the vehicle distance with a monocular camera. Our method is inexpensive and quite convenient to deploy on the mobile platforms. This paper makes two contributions. First, based on Light-Head RCNN, we propose a new vehicle detection framework called Light-Car Detection which can be used on the mobile platforms. Second, the planar homography of projective geometry is used to calculate the distance between the camera and the vehicles ahead. The results show that our detection system achieves 13FPS detection speed and 60.0% mAP on the Adreno 530 GPU of Samsung Galaxy S7, while only requires 7.1MB of storage space. Compared with the methods existed, the proposed method achieves a better performance.


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Cite this article

[IEEE Style]
Q. Ma, G. Jiang, D. Lai, H. cui, H. Song, "Fast, Accurate Vehicle Detection and Distance Estimation," KSII Transactions on Internet and Information Systems, vol. 14, no. 2, pp. 610-630, 2020. DOI: 10.3837/tiis.2020.02.008.

[ACM Style]
QuanMeng Ma, Guang Jiang, DianZhi Lai, Hua cui, and Huansheng Song. 2020. Fast, Accurate Vehicle Detection and Distance Estimation. KSII Transactions on Internet and Information Systems, 14, 2, (2020), 610-630. DOI: 10.3837/tiis.2020.02.008.

[BibTeX Style]
@article{tiis:23267, title="Fast, Accurate Vehicle Detection and Distance Estimation", author="QuanMeng Ma and Guang Jiang and DianZhi Lai and Hua cui and Huansheng Song and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2020.02.008}, volume={14}, number={2}, year="2020", month={February}, pages={610-630}}