{"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/contracting-skeletal-kinematic-embeddings-for","title":"Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection","arxiv_id":"2301.09489","date":"2023-01-23","proceeding":null,"authors":["Alessandro Flaborea","Guido D'Amely","Stefano D'arrigo","Marco Aurelio Sterpa","Alessio Sampieri","Fabio Galasso"],"abstract":"Detecting the anomaly of human behavior is paramount to timely recognizing endangering situations, such as street fights or elderly falls. However, anomaly detection is complex since anomalous events are rare and because it is an open set recognition task, i.e., what is anomalous at inference has not been observed at training. We propose COSKAD, a novel model that encodes skeletal human motion by a graph convolutional network and learns to COntract SKeletal kinematic embeddings onto a latent hypersphere of minimum volume for Video Anomaly Detection. We propose three latent spaces: the commonly-adopted Euclidean and the novel spherical and hyperbolic. All variants outperform the state-of-the-art on the most recent UBnormal dataset, for which we contribute a human-related version with annotated skeletons. COSKAD sets a new state-of-the-art on the human-related versions of ShanghaiTech Campus and CUHK Avenue, with performance comparable to video-based methods. Source code and dataset will be released upon acceptance.","url_abs":"https://arxiv.org/abs/2301.09489v5","url_pdf":"https://arxiv.org/pdf/2301.09489v5.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":"contracting-skeletal-kinematic-embeddings-for","repo_url":"https://github.com/aleflabo/COSKAD","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"open-set-learning","task_name":"Open Set Learning"},{"task_slug":"video-anomaly-detection","task_name":"Video Anomaly Detection"}],"methods":[],"datasets_introduced":[{"slug":"hr-ubnormal","name":"HR-UBnormal","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-ubnormal","task":"Anomaly Detection","dataset":"UBnormal","model":"COSKAD-hyperbolic","rank_in_archive_order":7,"of":14,"metrics":{"AUC":"65%"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-ubnormal","task":"Anomaly Detection","dataset":"UBnormal","model":"COSKAD-euclidean","rank_in_archive_order":8,"of":14,"metrics":{"AUC":"64.9%"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-ubnormal","task":"Anomaly Detection","dataset":"UBnormal","model":"COSKAD-radial","rank_in_archive_order":9,"of":14,"metrics":{"AUC":"62.9%"},"uses_additional_data":false},{"leaderboard":"/sota/video-anomaly-detection-on-hr-avenue","task":"Video Anomaly Detection","dataset":"HR-Avenue","model":"COSKAD-euclidean","rank_in_archive_order":4,"of":11,"metrics":{"AUC":"87.8"},"uses_additional_data":false},{"leaderboard":"/sota/video-anomaly-detection-on-hr-avenue","task":"Video Anomaly Detection","dataset":"HR-Avenue","model":"COSKAD-hyperbolic","rank_in_archive_order":5,"of":11,"metrics":{"AUC":"87.3"},"uses_additional_data":false},{"leaderboard":"/sota/video-anomaly-detection-on-hr-avenue","task":"Video Anomaly Detection","dataset":"HR-Avenue","model":"COSKAD-radial","rank_in_archive_order":10,"of":11,"metrics":{"AUC":"82.2"},"uses_additional_data":false},{"leaderboard":"/sota/video-anomaly-detection-on-hr-shanghaitech","task":"Video Anomaly Detection","dataset":"HR-ShanghaiTech","model":"COSKAD-euclidean","rank_in_archive_order":6,"of":14,"metrics":{"AUC":"77.1"},"uses_additional_data":false},{"leaderboard":"/sota/video-anomaly-detection-on-hr-shanghaitech","task":"Video Anomaly Detection","dataset":"HR-ShanghaiTech","model":"COSKAD-hyperbolic","rank_in_archive_order":8,"of":14,"metrics":{"AUC":"75.6"},"uses_additional_data":false},{"leaderboard":"/sota/video-anomaly-detection-on-hr-shanghaitech","task":"Video Anomaly Detection","dataset":"HR-ShanghaiTech","model":"COSKAD-radial","rank_in_archive_order":10,"of":14,"metrics":{"AUC":"75.2"},"uses_additional_data":false},{"leaderboard":"/sota/video-anomaly-detection-on-hr-ubnormal","task":"Video Anomaly Detection","dataset":"HR-UBnormal","model":"COSKAD-hyperbolic","rank_in_archive_order":3,"of":8,"metrics":{"AUC":"65.5"},"uses_additional_data":false},{"leaderboard":"/sota/video-anomaly-detection-on-hr-ubnormal","task":"Video Anomaly Detection","dataset":"HR-UBnormal","model":"COSKAD-euclidean","rank_in_archive_order":4,"of":8,"metrics":{"AUC":"65.2"},"uses_additional_data":false},{"leaderboard":"/sota/video-anomaly-detection-on-hr-ubnormal","task":"Video Anomaly Detection","dataset":"HR-UBnormal","model":"COSKAD-radial","rank_in_archive_order":5,"of":8,"metrics":{"AUC":"63.4"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2301.09489","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.09489"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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