{"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/generative-neural-networks-for-anomaly","title":"Generative Neural Networks for Anomaly Detection in Crowded Scenes","arxiv_id":null,"date":"2018-10-29","proceeding":null,"authors":["Tian Wang","Meina Qiao","Zhiwei Lin","Ce Li","Hichem Snoussi","Zhe Liu","Chang Choi"],"abstract":"Security surveillance is critical to social harmony and people's peaceful life. It has a great impact on strengthening social stability and life safeguarding. Detecting anomaly timely, effectively and efficiently in video surveillance remains challenging. This paper proposes a new approach, called S 2 -VAE, for anomaly detection from video data. The S 2 -VAE consists of two proposed neural networks: a Stacked Fully Connected Variational AutoEncoder (S F -VAE) and a Skip Convolutional VAE (S C -VAE). The S F -VAE is a shallow generative network to obtain a model like Gaussian mixture to fit the distribution of the actual data. The S C -VAE, as a key component of S 2 -VAE, is a deep generative network to take advantages of CNN, VAE and skip connections. Both S F -VAE and S C -VAE are efficient and effective generative networks and they can achieve better performance for detecting both local abnormal events and global abnormal events. The proposed S 2 -VAE is evaluated using four public datasets. The experimental results show that the S 2 -VAE outperforms the state-of-the-art algorithms. The code is available publicly at https://github.com/tianwangbuaa/.","url_abs":"https://ieeexplore.ieee.org/abstract/document/8513816","url_pdf":"https://ieeexplore.ieee.org/abstract/document/8513816","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":"generative-neural-networks-for-anomaly","repo_url":"https://github.com/tianwangbuaa/VAE-for-abnormal-event-detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"abnormal-event-detection-in-video","task_name":"Abnormal Event Detection In Video"},{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"semi-supervised-anomaly-detection","task_name":"Semi-supervised Anomaly Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/abnormal-event-detection-in-video-on-ubi","task":"Abnormal Event Detection In Video","dataset":"UBI-Fights","model":"s2-VAE","rank_in_archive_order":3,"of":6,"metrics":{"AUC":"0.610","Decidability":"0.323","EER":"0.427"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-anomaly-detection-on-ubi","task":"Semi-supervised Anomaly Detection","dataset":"UBI-Fights","model":"s2-VAE","rank_in_archive_order":5,"of":7,"metrics":{"AUC":"0.540","Decidability":"0.164","EER":"0.475"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}