{"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/adversarially-learned-abnormal-trajectory","title":"Adversarially Learned Abnormal Trajectory Classifier","arxiv_id":"1903.11040","date":"2019-03-26","proceeding":null,"authors":["Pankaj Raj Roy","Guillaume-Alexandre Bilodeau"],"abstract":"We address the problem of abnormal event detection from trajectory data. In\nthis paper, a new adversarial approach is proposed for building a deep neural\nnetwork binary classifier, trained in an unsupervised fashion, that can\ndistinguish normal from abnormal trajectory-based events without the need for\nsetting manual detection threshold. Inspired by the generative adversarial\nnetwork (GAN) framework, our GAN version is a discriminative one in which the\ndiscriminator is trained to distinguish normal and abnormal trajectory\nreconstruction errors given by a deep autoencoder. With urban traffic videos\nand their associated trajectories, our proposed method gives the best accuracy\nfor abnormal trajectory detection. In addition, our model can easily be\ngeneralized for abnormal trajectory-based event detection and can still yield\nthe best behavioural detection results as demonstrated on the CAVIAR dataset.","url_abs":"http://arxiv.org/abs/1903.11040v2","url_pdf":"http://arxiv.org/pdf/1903.11040v2.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":"adversarially-learned-abnormal-trajectory","repo_url":"https://github.com/proy3/Abnormal_Trajectory_Classifier","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"event-detection","task_name":"Event Detection"},{"task_slug":null,"task_name":"Generative Adversarial Network"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}