Detection of anomalous trajectories is an important problem with potential applications to various domains, such as video surveillance, risk assessment, vessel monitoring and high-energy physics. Modeling the distribution of trajectories with statistical approaches has been a challenging task due to the fact that such time series are usually non stationary and highly dimensional. However, modern machine learning techniques provide robust approaches for data-driven modeling and critical information extraction. In this paper, we propose a Sequence to Sequence architecture for real-time detection of anomalies in human trajectories, in the context of risk-based security. Our detection scheme is tested on a synthetic dataset of diverse and realistic trajectories generated by the ISL iCrowd simulator. The experimental results indicate that our scheme accurately detects motion patterns that deviate from normal behaviors and is promising for future real-world applications.
翻译:异常轨迹的探测是一个重要的问题,有可能应用于多个领域,例如视频监视、风险评估、船只监测和高能物理学。用统计方法模拟轨迹分布是一项艰巨的任务,因为此类时间序列通常是非固定和高度维度的。然而,现代机器学习技术为数据驱动的建模和关键信息提取提供了强有力的方法。在本文件中,我们提出了一个序列结构序列,以便在基于风险的安全背景下实时检测人类轨迹异常现象。我们的探测计划是用由ISL iCrowd模拟器生成的多种现实轨迹的合成数据集测试的。实验结果表明,我们的计划准确地检测了偏离正常行为的运动模式,并为未来的真实世界应用带来了希望。