基于物理的异常轨迹间隙检测

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Arun Sharma, Subhankar Ghosh, Shashi Shekhar
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引用次数: 0

摘要

鉴于轨迹存在间隙(即数据缺失),我们研究了识别轨迹异常间隙的算法,这种异常间隙发生在特定运动物体未报告其位置,但同一地理区域的其他运动物体定期报告其位置的情况下。这个问题的重要性在于它的社会应用,例如改善海上安全以及针对非法捕鱼、非法石油转移和转运等全球安全问题的监管执法。这个问题具有挑战性,因为在轨迹间隙期间很难限定移动物体的可能位置,而且在如此大量的位置数据中检测间隙的计算成本非常高。目前关于异常轨迹检测的文献假定在间隙内进行线性插值,这可能无法检测到异常间隙,因为给定区域内的物体可能已经偏离了其最短路径。在前期工作中,我们介绍了一种异常间隙测量方法,它使用经典的时空棱镜模型来约束物体在轨迹间隙期间的可能运动,并提供了一种可扩展的 memoized 间隙检测算法(Memo-AGD)。在本文中,我们提出了时空感知间隙检测(STAGD)方法,利用时空索引和轨迹间隙合并。我们还采用了基于动态区域合并(DRM)的方法来有效计算间隙异常得分。我们从理论上证明了这两种算法的正确性和完整性,并对渐近时间复杂性进行了分析。在合成和真实世界航海轨迹数据上的实验结果表明,与基线技术相比,所提出的方法大大缩短了计算时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Physics-based Abnormal Trajectory Gap Detection
Given trajectories with gaps (i.e., missing data), we investigate algorithms to identify abnormal gaps in trajectories which occur when a given moving object did not report its location, but other moving objects in the same geographic region periodically did. The problem is important due to its societal applications, such as improving maritime safety and regulatory enforcement for global security concerns such as illegal fishing, illegal oil transfers, and trans-shipments. The problem is challenging due to the difficulty of bounding the possible locations of the moving object during a trajectory gap, and the very high computational cost of detecting gaps in such a large volume of location data. The current literature on anomalous trajectory detection assumes linear interpolation within gaps, which may not be able to detect abnormal gaps since objects within a given region may have traveled away from their shortest path. In preliminary work, we introduced an abnormal gap measure that uses a classical space-time prism model to bound an object’s possible movement during the trajectory gap and provided a scalable memoized gap detection algorithm (Memo-AGD). In this paper, we propose a Space Time-Aware Gap Detection (STAGD) approach to leverage space-time indexing and merging of trajectory gaps. We also incorporate a Dynamic Region Merge-based (DRM) approach to efficiently compute gap abnormality scores. We provide theoretical proofs that both algorithms are correct and complete and also provide analysis of asymptotic time complexity. Experimental results on synthetic and real-world maritime trajectory data show that the proposed approach substantially improves computation time over the baseline technique.
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来源期刊
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.30
自引率
2.00%
发文量
131
期刊介绍: ACM Transactions on Intelligent Systems and Technology is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly (six issues a year). Each issue has 8-11 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers.
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