{"type":"ScholarlyArticle","title":"Denoising Diffusion-Augmented Hybrid Video Anomaly Detection via Reconstructing Noised Frames","authors":[{"name":"Kai Cheng","slug":"kai-cheng","given":"Kai","family":"Cheng","affiliation":"Academy for Engineering and Technology , Fudan University"},{"name":"Yaning Pan","slug":"yaning-pan","given":"Yaning","family":"Pan","affiliation":"School of Computer Science , Shanghai Key Laboratory of Intelligent Information Processing , Fudan University"},{"name":"Yang Liu","slug":"yang-liu","given":"Yang","family":"Liu","affiliation":"Academy for Engineering and Technology , Fudan University; Department of Computer Science , University of Toronto {chengkai1729 ,"},{"name":"Xinhua Zeng","slug":"xinhua-zeng","given":"Xinhua","family":"Zeng","affiliation":"Academy for Engineering and Technology , Fudan University"},{"name":"Rui Feng","slug":"rui-feng","given":"Rui","family":"Feng","affiliation":"School of Computer Science , Shanghai Key Laboratory of Intelligent Information Processing , Fudan University"}],"abstract":"Video Anomaly Detection (VAD) is crucial for enhancing security and surveillance systems through automatic identification of irregular events, thereby enabling timely responses and augmenting overall situational awareness. Although existing methods have achieved decent detection performances on benchmarks, their predicted objects still remain ambiguous in terms of the semantic aspect. To overcome this limitation, we propose the Denoising diffusion-augmented Hybrid Video Anomaly Detection (DHVAD) framework. The proposed Denoising diffusion-based Reconstruction Unit (DRU) enhances the understanding of semantically accurate normality as a crucial component in DHVAD. Meanwhile, we propose a detection strategy that integrates the advantages of a prediction-based Frame Prediction Unit (FPU) with DRU by exploring the spatial-temporal consistency seamlessly. The competitive performance of DHVAD compared with state-of-the-art methods on three benchmark datasets proves the effectiveness of our framework. The extended experimental analysis demonstrates that our framework can gain a better understanding of the normality in terms of semantic accuracy for VAD and efficiently leverage the strengths of both components.","doi":"10.24963/ijcai.2024/77","url":"https://ijcai.org/proceedings/2024/77","pdf":"https://ijcai.org/proceedings/2024/0077.pdf","markdown":"https://ijcai.org/proceedings/2024/77.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/77","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":77,"pages":{"first":695,"last":703},"keywords":["Computer Vision: CV: Representation learning","Computer Vision: CV: Scene analysis and understanding","Computer Vision: CV: Video analysis and understanding"],"videos":[],"references":[{"position":1,"raw":"[Cai and Vasconcelos, 2019] Zhaowei Cai and Nuno Vas- concelos. 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