@inproceedings{d492aa3167654c8cb49d3432c6377daa,
title = "SAVeD: Acoustic Vehicle Detector with Speed Estimation capable of Sequential Vehicle Detection",
abstract = "In the ITS (intelligent transportation system), vehicle detection is one of the core technologies. We are developing an acoustic vehicle detector that detects vehicles using a sound map, which is a map of sound arrival time difference on two microphones. We developed vehicle detection algorithms based on state machine and DTW (dynamic time warping) to detect S-curves on a sound map drawn by passing vehicles. However, the detection algorithms often fail to detect simultaneous and sequential passing vehicles. This paper presents SAVeD, a sequential acoustic vehicle detector. The SAVeD fits an S-curve model to sound map points using a RANSAC (random sample consensus) robust estimation method to detect each vehicle. The SAVeD then removes sound map points corresponding to the detected vehicle and continues vehicle detection process for the following vehicles. Experimental evaluations demonstrated that the SAVeD improves detection accuracy by more than 10 points compared to the state-machine based algorithm.",
author = "Shigemi Ishida and Jumpei Kajimura and Masato Uchino and Shigeaki Tagashira and Akira Fukuda",
year = "2018",
month = dec,
day = "7",
doi = "10.1109/ITSC.2018.8569727",
language = "English",
series = "IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "906--912",
booktitle = "2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018",
address = "United States",
note = "21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018 ; Conference date: 04-11-2018 Through 07-11-2018",
}