{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/road-user-abnormal-trajectory-detection-using","title":"Road User Abnormal Trajectory Detection using a Deep Autoencoder","arxiv_id":"1809.00957","date":"2018-08-25","proceeding":null,"authors":["Pankaj Raj Roy","Guillaume-Alexandre Bilodeau"],"abstract":"In this paper, we focus on the development of a method that detects abnormal\ntrajectories of road users at traffic intersections. The main difficulty with\nthis is the fact that there are very few abnormal data and the normal ones are\ninsufficient for the training of any kinds of machine learning model. To tackle\nthese problems, we proposed the solution of using a deep autoencoder network\ntrained solely through augmented data considered as normal. By generating\nartificial abnormal trajectories, our method is tested on four different\noutdoor urban users scenes and performs better compared to some classical\noutlier detection methods.","url_abs":"http://arxiv.org/abs/1809.00957v1","url_pdf":"http://arxiv.org/pdf/1809.00957v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"road-user-abnormal-trajectory-detection-using","repo_url":"https://github.com/proy3/Abnormal_Trajectory_Classifier","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"outlier-detection","task_name":"Outlier Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}