@inproceedings{a375e4607d6842449197b0fb885aa855,
title = "Anomaly Detection for Quaternion-Valued Traffic Signals",
abstract = "In this paper, a novel anomaly detection method ispresented by using quaternion numbers to model traffic signals. A signal processing approach is proposed to deal with traffic surveillance. Traffic structures are depicted using directed graph models. The relationship among different traffic direction signals are represented through using quaternion numbers instead of individual representation of one particular direction. Multi- granularity local density-based method is adopted to perform anomaly detection for separate entry direction distribution (EDD) signals. Complex traffic signals are subsequently examined by exploring the relationship expressed with quaternion numbers. In such way, the anomaly detection complexity is reduced. Experimental results show that the proposed algorithm can achieve high detection rate. The overall average DSR of both AM and PM sessions is about 97.83%, which is better than the previous algorithm (96.67%) in the literature.",
keywords = "Anomaly detection, density based method, directed graph, quaternion, traffic data, traffic surveillance",
author = "Wang, {Li Li} and Ngan, {Henry Y.T.} and Wei Liu and Yung, {Nelson H.C.}",
year = "2016",
month = dec,
day = "22",
doi = "10.1109/DICTA.2016.7797009",
language = "English",
series = "2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016",
publisher = "IEEE",
editor = "Liew, {Alan Wee-Chung} and Jun Zhou and Yongsheng Gao and Zhiyong Wang and Clinton Fookes and Brian Lovell and Michael Blumenstein",
booktitle = "2016 International Conference on Digital Image Computing",
address = "United States",
note = "2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016 ; Conference date: 30-11-2016 Through 02-12-2016",
}
