{"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/for-the-sake-of-privacy-skeleton-based","title":"FOR THE SAKE OF PRIVACY: SKELETON-BASED SALIENT BEHAVIOR RECOGNITION","arxiv_id":null,"date":"2022-10-18","proceeding":"2022 IEEE International Conference on Image Processing (ICIP) 2022 10","authors":["Thomas Golda","Johanna Thiemich","Mickael Cormier","Jurgen Beyerer"],"abstract":"Authorities as well as emergency and rescue services have an increasing interest in smart support systems to ensure public safety which includes in particular behavioral analysis of pedestrians by using video surveillance systems. In order to accommodate concerns of citizens regarding their personal rights, the demand for data privacy friendly approaches, using as few information as possible, arises. In this paper, we examine existing approaches tackling the recognition of anomalous or salient behavior based solely on person pose information within the context of real-world surveillance applications. Particularly, we chose two existing state-of-the-art approaches and evaluate them on two public and an internal dataset in order to examine the overall performance of these methods for the desired task. Furthermore, we present our\r\nown approach achieving comparable results to these methods. Finally, we extend the aforementioned methods with a memory extension for modeling normal behavior, which yields on average a 4.3% higher recognition performance.","url_abs":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9897358","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9897358","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":[],"tasks":[{"task_slug":"anomaly-detection-in-surveillance-videos","task_name":"Anomaly Detection In Surveillance Videos"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-in-surveillance-videos-on-5","task":"Anomaly Detection In Surveillance Videos","dataset":"VFP290K","model":"MPED-RNN (with memory extension)","rank_in_archive_order":2,"of":4,"metrics":{"ROC AUC":"96.02"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-in-surveillance-videos-on-5","task":"Anomaly Detection In Surveillance Videos","dataset":"VFP290K","model":"MPED-RNN","rank_in_archive_order":3,"of":4,"metrics":{"ROC AUC":"69.34"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-in-surveillance-videos-on-5","task":"Anomaly Detection In Surveillance Videos","dataset":"VFP290K","model":"GEPC","rank_in_archive_order":4,"of":4,"metrics":{"ROC AUC":"64.03"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}