Abstract
With recent advances in sensory and mobile computing technology, many interesting applications involving moving objects have emerged. One of them is identification of suspicious movements : an important problem in homeland security. The objects in question can be vehicles, airplanes, or ships; however, due to the sheer volume of data and the complexities within, manual inspection of the moving objects would require too much manpower. Thus, an automated or semi-automated solution to this problem would be very helpful. That said, it is challenging to develop a method that can efficiently and effectively detect anomalies. The problem is exacerbated by the fact that anomalies may occur at arbitrary levels of abstraction and be associated with multiple granularity of spatiotemporal features. In this study, we propose a new framework named ROAM (Rule- and Motif-based Anomaly Detection in M oving Objects). In ROAM, object trajectories are expressed using discrete pattern fragments called motifs. Associated features are extracted to form a hierarchical feature space, which facilitates a multi-resolution view of the data. We also develop a general-purpose, rule-based classifier which explores the structured feature space and learns effective rules at multiple levels of granularity. Such rules are easily readable and can be easily provided to humans to aid better inspection of moving objects.
| Original language | English (US) |
|---|---|
| Title of host publication | Intelligence and Security Informatics |
| Subtitle of host publication | Techniques and Applications |
| Editors | Hsinchun Chen, Christopher Yang |
| Pages | 357-381 |
| Number of pages | 25 |
| DOIs | |
| State | Published - Jul 3 2008 |
Publication series
| Name | Studies in Computational Intelligence |
|---|---|
| Volume | 135 |
| ISSN (Print) | 1860-949X |
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ASJC Scopus subject areas
- Artificial Intelligence
Cite this
Anomaly detection in moving object. / Li, Xiaolei; Han, Jiawei; Kim, Sangkyum; Gonzalez, Hector.
Intelligence and Security Informatics: Techniques and Applications. ed. / Hsinchun Chen; Christopher Yang. 2008. p. 357-381 (Studies in Computational Intelligence; Vol. 135).Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Anomaly detection in moving object
AU - Li, Xiaolei
AU - Han, Jiawei
AU - Kim, Sangkyum
AU - Gonzalez, Hector
PY - 2008/7/3
Y1 - 2008/7/3
N2 - With recent advances in sensory and mobile computing technology, many interesting applications involving moving objects have emerged. One of them is identification of suspicious movements : an important problem in homeland security. The objects in question can be vehicles, airplanes, or ships; however, due to the sheer volume of data and the complexities within, manual inspection of the moving objects would require too much manpower. Thus, an automated or semi-automated solution to this problem would be very helpful. That said, it is challenging to develop a method that can efficiently and effectively detect anomalies. The problem is exacerbated by the fact that anomalies may occur at arbitrary levels of abstraction and be associated with multiple granularity of spatiotemporal features. In this study, we propose a new framework named ROAM (Rule- and Motif-based Anomaly Detection in M oving Objects). In ROAM, object trajectories are expressed using discrete pattern fragments called motifs. Associated features are extracted to form a hierarchical feature space, which facilitates a multi-resolution view of the data. We also develop a general-purpose, rule-based classifier which explores the structured feature space and learns effective rules at multiple levels of granularity. Such rules are easily readable and can be easily provided to humans to aid better inspection of moving objects.
AB - With recent advances in sensory and mobile computing technology, many interesting applications involving moving objects have emerged. One of them is identification of suspicious movements : an important problem in homeland security. The objects in question can be vehicles, airplanes, or ships; however, due to the sheer volume of data and the complexities within, manual inspection of the moving objects would require too much manpower. Thus, an automated or semi-automated solution to this problem would be very helpful. That said, it is challenging to develop a method that can efficiently and effectively detect anomalies. The problem is exacerbated by the fact that anomalies may occur at arbitrary levels of abstraction and be associated with multiple granularity of spatiotemporal features. In this study, we propose a new framework named ROAM (Rule- and Motif-based Anomaly Detection in M oving Objects). In ROAM, object trajectories are expressed using discrete pattern fragments called motifs. Associated features are extracted to form a hierarchical feature space, which facilitates a multi-resolution view of the data. We also develop a general-purpose, rule-based classifier which explores the structured feature space and learns effective rules at multiple levels of granularity. Such rules are easily readable and can be easily provided to humans to aid better inspection of moving objects.
UR - http://www.scopus.com/inward/record.url?scp=45949111626&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=45949111626&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-69209-6_19
DO - 10.1007/978-3-540-69209-6_19
M3 - Chapter
AN - SCOPUS:45949111626
SN - 9783540692072
T3 - Studies in Computational Intelligence
SP - 357
EP - 381
BT - Intelligence and Security Informatics
A2 - Chen, Hsinchun
A2 - Yang, Christopher
ER -