{"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/real-time-anomaly-detection-and-feature","title":"Real-Time Anomaly Detection and Feature Analysis Based on Time Series for Surveillance Video","arxiv_id":null,"date":"2021-05-11","proceeding":null,"authors":["Ruoyu Xue","Jingyuan Chen","Yajun Fang"],"abstract":"The intelligent surveillance system urgently needs the real-time machine recognition of abnormal events to solve\r\nthe extremely uneven human supervision resource and digital cameras. Besides, the number of anomaly types that real-time\r\nmachine monitoring could recognize has not met the need. This paper presents a fast and robust methodology for real-time\r\nanomaly detection under different scenarios. We created the Video-Energy-Vector(VEV) to significantly reduce the dimension of feature maps while maintaining the spatial-temporal information. We applied the proposed method on different computer vision features to evaluate the effectiveness of common features to different types of abnormal events based on SVM. Also, we adopted the voting model among different features, which significantly increased the performance. Further More, the small video size to be trained guaranteed the real-time efficiency. The result of the modified UCF-Crime Dataset has proved that our approach has achieved robust results and had the generalization ability on new anomaly types.","url_abs":"https://ieeexplore.ieee.org/abstract/document/9426191","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9426191","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":"real-time-anomaly-detection-and-feature","repo_url":"https://github.com/jingyuanchan/Real-time-video-anomaly-detection","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"anomaly-detection-in-surveillance-videos","task_name":"Anomaly Detection In Surveillance Videos"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[{"method_slug":"base-boosting","method_name":"Base Boosting"},{"method_slug":"feature-centric-voting","method_name":"Feature-Centric Voting"},{"method_slug":"hierarchical-feature-fusion","method_name":"Hierarchical Feature Fusion"},{"method_slug":"svm","method_name":"SVM"}],"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}