Back to articles
Articles
Volume: 28 | Article ID: art00003
Image
Density-based Outlier Detection by Local Outlier Factor on Largescale Traffic Data
  DOI :  10.2352/ISSN.2470-1173.2016.14.IPMVA-385  Published OnlineFebruary 2016
Abstract

A density-based outlier detection (OD) method is presented by measuring the local outlier factor (LOF) on a projected principal component analysis (PCA) domain from real world spatialtemporal (ST) traffic signals. Its aim is to detect traffic data outliers which are errors in data and traffic anomalies in real situations such as accidents, congestions and low volume. Since the ST traffic signals have a high degree of similarities, they are first projected to two-dimensional (2D) (x,y)-coordinates by the PCA to reduce its dimension as well as to remove noise, while keeping the anomaly information of the signals. Based on the designed LOF algorithm, a semi-supervised approach is employed to label any embedded outliers. It reaches an average detection success rate of 93.5%.

Subject Areas :
Views 93
Downloads 4
 articleview.views 93
 articleview.downloads 4
  Cite this article 

Mathew X Ma, Henry Y.T Ngan, Wei Liu, "Density-based Outlier Detection by Local Outlier Factor on Largescale Traffic Datain Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Processing: Machine Vision Applications IX,  2016,  https://doi.org/10.2352/ISSN.2470-1173.2016.14.IPMVA-385

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2016
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151 USA