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Proceedings Paper

Sparsity-driven anomaly detection for ship detection and tracking in maritime video
Author(s): Scott Shafer; Josh Harguess; Pedro A. Forero
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Paper Abstract

This work examines joint anomaly detection and dictionary learning approaches for identifying anomalies in persistent surveillance applications that require data compression. We have developed a sparsity-driven anomaly detector that can be used for learning dictionaries to address these challenges. In our approach, each training datum is modeled as a sparse linear combination of dictionary atoms in the presence of noise. The noise term is modeled as additive Gaussian noise and a deterministic term models the anomalies. However, no model for the statistical distribution of the anomalies is made. An estimator is postulated for a dictionary that exploits the fact that since anomalies by definition are rare, only a few anomalies will be present when considering the entire dataset. From this vantage point, we endow the deterministic noise term (anomaly-related) with a group-sparsity property. A robust dictionary learning problem is postulated where a group-lasso penalty is used to encourage most anomaly-related noise components to be zero. The proposed estimator achieves robustness by both identifying the anomalies and removing their effect from the dictionary estimate. Our approach is applied to the problem of ship detection and tracking from full-motion video with promising results.

Paper Details

Date Published: 22 May 2015
PDF: 8 pages
Proc. SPIE 9476, Automatic Target Recognition XXV, 947608 (22 May 2015); doi: 10.1117/12.2178417
Show Author Affiliations
Scott Shafer, Space and Naval Warfare Systems Ctr. Pacific (United States)
Josh Harguess, Space and Naval Warfare Systems Ctr. Pacific (United States)
Pedro A. Forero, Space and Naval Warfare Systems Ctr. Pacific (United States)

Published in SPIE Proceedings Vol. 9476:
Automatic Target Recognition XXV
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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