Detection of suspicious activity using incremental outlier detection algorithms

D. Pokrajac; N. Reljin; N. Pejcic; T. Vance; S. McDaniel; A. Lazarevic; H. J. Chang; J. Y. Choi; R. Miezianko

doi:10.1117/12.828701

3 September 2009 Detection of suspicious activity using incremental outlier detection algorithms

D. Pokrajac, N. Reljin, N. Pejcic, T. Vance, S. McDaniel, A. Lazarevic, H. J. Chang, J. Y. Choi, R. Miezianko

Proceedings Volume 7445, Signal and Data Processing of Small Targets 2009; 744509 (2009) https://doi.org/10.1117/12.828701
Event: SPIE Optical Engineering + Applications, 2009, San Diego, California, United States

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.

Citation Download Citation

D. Pokrajac, N. Reljin, N. Pejcic, T. Vance, S. McDaniel, A. Lazarevic, H. J. Chang, J. Y. Choi, and R. Miezianko "Detection of suspicious activity using incremental outlier detection algorithms", Proc. SPIE 7445, Signal and Data Processing of Small Targets 2009, 744509 (3 September 2009); https://doi.org/10.1117/12.828701

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