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Sparsity-driven Anomaly Detection for Ship Detection and Tracking in Maritime Video

机译：稀疏驱动的异常检测在海上视频中的船舶检测和跟踪

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摘要

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. Prom 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.
机译：这项工作研究了联合异常检测和字典学习方法，以识别需要数据压缩的持续监视应用程序中的异常。我们已经开发了一种稀疏驱动的异常检测器，可用于学习字典来应对这些挑战。在我们的方法中，每个训练数据都被建模为在存在噪声的情况下字典原子的稀疏线性组合。噪声项被建模为加性高斯噪声，而确定项则对异常进行建模。但是，没有为异常的统计分布建立模型。一个字典的估计量是假定的，该字典利用了这样一个事实：由于从定义上讲异常很少见，因此在考虑整个数据集时仅会出现少数异常。提示这个优势，我们赋予确定性噪声项（与异常有关）具有群稀疏性。假设存在鲁棒的字典学习问题，其中使用组套索惩罚来鼓励大多数与异常相关的噪声分量为零。所提出的估计器通过识别异常并从字典估计中消除其影响来实现鲁棒性。我们的方法应用于从全动态视频进行船舶检测和跟踪的问题，并取得了可喜的结果。

著录项

  • 来源
    《Automatic Target Recognition XXV》|2015年|947608.1-947608.8|共8页
  • 会议地点 Baltimore MD(US)
  • 作者

    Scott Shafer; Josh Harguess; Pedro A. Forero;

  • 作者单位

    Space and Naval Warfare Systems Center Pacific, 53560 Hull St., San Diego, CA, USA;

    Space and Naval Warfare Systems Center Pacific, 53560 Hull St., San Diego, CA, USA;

    Space and Naval Warfare Systems Center Pacific, 53560 Hull St., San Diego, CA, USA;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    sparse group lasso; anomaly detection;

    机译：稀疏组套索;异常检测;

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