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文件名称：Online Learning and Sequential Anomaly Detection in Trajectories
文件大小：2.95MB
文件格式：PDF
更新时间：2022-03-26 07:35:49
机器学习 人工智能
Detection of anomalous trajectories is an important problem in the surveillance domain. Various algorithms based on
learning of normal trajectory patterns have been proposed for this problem. Yet, these algorithms typically suffer from one or more
limitations: They are not designed for sequential analysis of incomplete trajectories or online learning based on an incrementally
updated training set. Moreover, they typically involve tuning of many parameters, including ad-hoc anomaly thresholds, and may
therefore suffer from overfitting and poorly-calibrated alarm rates.