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Proceedings Paper

Detection of suspicious activity using incremental outlier detection algorithms
Author(s): D. Pokrajac; N. Reljin; N. Pejcic; T. Vance; S. McDaniel; A. Lazarevic; H. J. Chang; J. Y. Choi; R. Miezianko
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Paper Abstract

Detection of unusual trajectories of moving objects can help in identifying suspicious activity on convoy routes and thus reduce casualties caused by improvised explosive devices. In this paper, using video imagery we compare efficiency of various techniques for incremental outlier detection on detecting unusual trajectories on simulated and real-life data obtained from SENSIAC database. Incremental outlier detection algorithms that we consider in this paper include incremental Support Vector Classifier (incSVC), incremental Local Outlier Factor (incLOF) algorithm and incremental Connectivity Outlier Factor (incCOF) algorithm. Our experiments performed on ground truth trajectory data indicate that incremental LOF algorithm can provide better detection of unusual trajectories in comparison to other examined techniques.

Paper Details

Date Published: 3 September 2009
PDF: 12 pages
Proc. SPIE 7445, Signal and Data Processing of Small Targets 2009, 744509 (3 September 2009); doi: 10.1117/12.828701
Show Author Affiliations
D. Pokrajac, Delaware State Univ. (United States)
N. Reljin, Delaware State Univ. (United States)
N. Pejcic, Delaware State Univ. (United States)
T. Vance, Delaware State Univ. (United States)
S. McDaniel, Delaware State Univ. (United States)
A. Lazarevic, United Technologies Research Ctr. (United States)
H. J. Chang, Seoul National Univ. (Korea, Republic of)
J. Y. Choi, Seoul National Univ. (Korea, Republic of)
R. Miezianko, Lockheed Martin (United States)

Published in SPIE Proceedings Vol. 7445:
Signal and Data Processing of Small Targets 2009
Oliver E. Drummond; Richard D. Teichgraeber, Editor(s)

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