{"type":"ScholarlyArticle","title":"Open Anomalous Trajectory Recognition via Probabilistic Metric Learning","authors":[{"name":"Qiang Gao","slug":"qiang-gao","given":"Qiang","family":"Gao","affiliation":"Southwestern University of Finance and Economics , Chengdu , China, 611130"},{"name":"Xiaohan Wang","slug":"xiaohan-wang","given":"Xiaohan","family":"Wang","affiliation":"University of Electronic Science and Technology of China , Chengdu , China, 610054"},{"name":"Chaoran Liu","slug":"chaoran-liu","given":"Chaoran","family":"Liu","affiliation":"Southwestern University of Finance and Economics , Chengdu , China, 611130"},{"name":"Goce Trajcevski","slug":"goce-trajcevski","given":"Goce","family":"Trajcevski","affiliation":"Iowa State University , Iowa , USA"},{"name":"Li Huang","slug":"li-huang","given":"Li","family":"Huang","affiliation":"Southwestern University of Finance and Economics , Chengdu , China, 611130"},{"name":"Fan Zhou","slug":"fan-zhou","given":"Fan","family":"Zhou","affiliation":"University of Electronic Science and Technology of China , Chengdu , China, 610054"}],"abstract":"Typically, trajectories considered anomalous are the ones deviating from usual (e.g., traffic-dictated) driving patterns. However, this closed-set context fails to recognize the unknown anomalous trajectories, resulting in an insufficient self-motivated learning paradigm. In this study, we investigate the novel Anomalous Trajectory Recognition problem in an Open-world scenario (ATRO) and introduce a novel probabilistic Metric learning model, namely ATROM, to address it. Specifically, ATROM can detect the presence of unknown anomalous behavior in addition to identifying known behavior. It has a Mutual Interaction Distillation that uses contrastive metric learning to explore the interactive semantics regarding the diverse behavioral intents and a Probabilistic Trajectory Embedding that forces the trajectories with distinct behaviors to follow different Gaussian priors. More importantly, ATROM offers a probabilistic metric rule to discriminate between known and unknown behavioral patterns by taking advantage of the approximation of multiple priors. Experimental results on two large-scale trajectory datasets demonstrate the superiority of ATROM in addressing both known and unknown anomalous patterns.","doi":"10.24963/ijcai.2023/233","url":"https://ijcai.org/proceedings/2023/233","pdf":"https://ijcai.org/proceedings/2023/0233.pdf","markdown":"https://ijcai.org/proceedings/2023/233.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/233","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Data Mining","track":"Main Track","number":233,"pages":{"first":2095,"last":2103},"keywords":["Data Mining: DM: Mining spatial and/or temporal data","Data Mining: DM: Applications","Multidisciplinary Topics and Applications: MDA: Transportation"],"videos":[],"references":[{"position":1,"raw":"[Belhadi et al., 2020] Asma Belhadi, Youcef Djenouri, Jerry Chun-Wei Lin, and Alberto Cano. 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