{"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/vald-gan-video-anomaly-detection-using-latent","title":"VALD-GAN: video anomaly detection using latent discriminator augmented GAN","arxiv_id":null,"date":"2023-10-18","proceeding":"Signal, Image and Video Processing 2023 10","authors":["Rituraj Singh","Anikeit Sethi","Krishanu Saini","Sumeet Saurav","Aruna Tiwari","Sanjay Singh"],"abstract":"The most crucial and difficult challenge for intelligent video surveillance is to identify anomalies in a video that comprises anomalous behavior or occurrences. The ambiguous definition of the anomaly makes the detection of it a challenging task. Inspired by the wide adoption of generative adversarial networks (GANs), we proposed video anomaly detection using latent discriminator augmented GAN (VALD-GAN), which combines the representation power of GANs with a novel latent discriminator framework to make the latent space follow a pre-defined distribution. We show through our experimental results that the proposed method significantly increases the anomaly discrimination capability of the model. VALD-GAN achieves an AUC and EER score of 97.98, 6.0% on UCSD Peds1, 97.74, 7.01% on UCSD Peds2, and 91.03, 9.04% on CUHK Avenue dataset, respectively. Also, it is able to detect 62 out of a total of 66 anomalous events with 4 as false alarms and 19 out of a total of 19 with 1 false alarm from Subway Entrance and Exit video datasets, respectively.","url_abs":"https://doi.org/10.1007/s11760-023-02750-5","url_pdf":"https://doi.org/10.1007/s11760-023-02750-5","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","task_name":"Anomaly Detection"},{"task_slug":"anomaly-detection-in-surveillance-videos","task_name":"Anomaly Detection In Surveillance Videos"},{"task_slug":"video-anomaly-detection","task_name":"Video Anomaly Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-chuk-avenue","task":"Anomaly Detection","dataset":"CUHK Avenue","model":"VALD-GAN","rank_in_archive_order":16,"of":35,"metrics":{"AUC":"91.03"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-ucsd-ped2","task":"Anomaly Detection","dataset":"UCSD Ped2","model":"VALD-GAN","rank_in_archive_order":8,"of":14,"metrics":{"AUC":"97.74"},"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}