Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Connecting a Digital Europe Through Location and Place |
| Herausgeber (Verlag) | Kluwer Academic Publishers |
| Seiten | 91-104 |
| Seitenumfang | 14 |
| ISBN (elektronisch) | 9783319036106 |
| Publikationsstatus | Veröffentlicht - 18 Mai 2014 |
| Veranstaltung | 17th AGILE Conference on Geographic Information Science, AGILE 2014 - Castellon, Spanien Dauer: 1 März 2014 → … |
Publikationsreihe
| Name | Lecture Notes in Geoinformation and Cartography |
|---|---|
| ISSN (Print) | 1863-2351 |
Abstract
This chapter presents an original approach to anomalous behavior analysis in trajectory data by means of a recursive Bayesian filter. The anomalous pattern detection is of great interest in the areas of navigation, driver assistant system, surveillance and emergencymanagement. In this work we focus on the GPS trajectories finding where the driver is encountering navigation problems, i.e., taking awrong turn, performing a detour or tending to lose his way. To extract the related features, i.e., turns and their density, degree of detour and route repetition, a long-term perspective is required to observe data sequences instead of individual data points. We therefore employ high-order Markov chain to remodel the trajectory integrating these long-term features. A recursive Bayesian filter is conducted to process the Markov model and deliver an optimal probability distribution of the potential anomalous driving behaviors dynamically over time. The proposed filter performs unsupervised detection in single trajectory with solely the local features. No training process is required to characterize the anomalous behaviors. Based on the results of individual trajectories collective behaviors can be analyzed as well to indicate some traffic issues, e.g., turn restriction, blind alley, temporary road-block, etc. Experiments are performed on the trajectory data in urban areas demonstrating the potential of this approach.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Tief- und Ingenieurbau
- Sozialwissenschaften (insg.)
- Geografie, Planung und Entwicklung
- Erdkunde und Planetologie (insg.)
- Erdoberflächenprozesse
- Erdkunde und Planetologie (insg.)
- Computer in den Geowissenschaften
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- BibTex
- RIS
Connecting a Digital Europe Through Location and Place. Kluwer Academic Publishers, 2014. S. 91-104 (Lecture Notes in Geoinformation and Cartography).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - A recursive bayesian filter for anomalous behavior detection in trajectory data
AU - Huang, Hai
AU - Zhang, Lijuan
AU - Sester, Monika
PY - 2014/5/18
Y1 - 2014/5/18
N2 - This chapter presents an original approach to anomalous behavior analysis in trajectory data by means of a recursive Bayesian filter. The anomalous pattern detection is of great interest in the areas of navigation, driver assistant system, surveillance and emergencymanagement. In this work we focus on the GPS trajectories finding where the driver is encountering navigation problems, i.e., taking awrong turn, performing a detour or tending to lose his way. To extract the related features, i.e., turns and their density, degree of detour and route repetition, a long-term perspective is required to observe data sequences instead of individual data points. We therefore employ high-order Markov chain to remodel the trajectory integrating these long-term features. A recursive Bayesian filter is conducted to process the Markov model and deliver an optimal probability distribution of the potential anomalous driving behaviors dynamically over time. The proposed filter performs unsupervised detection in single trajectory with solely the local features. No training process is required to characterize the anomalous behaviors. Based on the results of individual trajectories collective behaviors can be analyzed as well to indicate some traffic issues, e.g., turn restriction, blind alley, temporary road-block, etc. Experiments are performed on the trajectory data in urban areas demonstrating the potential of this approach.
AB - This chapter presents an original approach to anomalous behavior analysis in trajectory data by means of a recursive Bayesian filter. The anomalous pattern detection is of great interest in the areas of navigation, driver assistant system, surveillance and emergencymanagement. In this work we focus on the GPS trajectories finding where the driver is encountering navigation problems, i.e., taking awrong turn, performing a detour or tending to lose his way. To extract the related features, i.e., turns and their density, degree of detour and route repetition, a long-term perspective is required to observe data sequences instead of individual data points. We therefore employ high-order Markov chain to remodel the trajectory integrating these long-term features. A recursive Bayesian filter is conducted to process the Markov model and deliver an optimal probability distribution of the potential anomalous driving behaviors dynamically over time. The proposed filter performs unsupervised detection in single trajectory with solely the local features. No training process is required to characterize the anomalous behaviors. Based on the results of individual trajectories collective behaviors can be analyzed as well to indicate some traffic issues, e.g., turn restriction, blind alley, temporary road-block, etc. Experiments are performed on the trajectory data in urban areas demonstrating the potential of this approach.
UR - http://www.scopus.com/inward/record.url?scp=84924733633&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-03611-3_6
DO - 10.1007/978-3-319-03611-3_6
M3 - Conference contribution
AN - SCOPUS:84924733633
T3 - Lecture Notes in Geoinformation and Cartography
SP - 91
EP - 104
BT - Connecting a Digital Europe Through Location and Place
PB - Kluwer Academic Publishers
T2 - 17th AGILE Conference on Geographic Information Science, AGILE 2014
Y2 - 1 March 2014
ER -