{"type":"ScholarlyArticle","title":"Long Short-Term Dynamic Prototype Alignment Learning for Video Anomaly Detection","authors":[{"name":"Chao Huang","slug":"chao-huang","given":"Chao","family":"Huang","affiliation":"School of Cyber Science and Technology , Shenzhen Campus of Sun Yat-sen University"},{"name":"Jie Wen","slug":"jie-wen","given":"Jie","family":"Wen","affiliation":"School of Computer Science and Technology , Harbin Institute of Technology , Shenzhen"},{"name":"Chengliang Liu","slug":"chengliang-liu","given":"Chengliang","family":"Liu","affiliation":"School of Computer Science and Technology , Harbin Institute of Technology , Shenzhen"},{"name":"Yabo Liu","slug":"yabo-liu","given":"Yabo","family":"Liu","affiliation":"School of Computer Science and Technology , Harbin Institute of Technology , Shenzhen"}],"abstract":"Video anomaly detection (VAD) is the core problem of intelligent video surveillance. Previous methods commonly adopt the unsupervised paradigm of frame reconstruction or prediction. However, the lack of mining of temporal dependent relationships and diversified event patterns within videos limit the performance of existing methods. To tackle these problems, we propose a novel prototype-guided and dynamic-aware long-distance frame prediction paradigm for VAD. Specifically, we develop a prototype-guided dynamics matching network (PDM-Net) to enhance the discriminant and robustness of anomaly detector. To explore the temporal contexts, we equip PDM-Net with a long short-term dynamic prototype alignment learning mechanism, which stores long-term dynamic prototypes into memory bank and learns how to recall long-term dynamic prototypes with short-term dynamics. As a result, the short input sequences can recall long-term dynamic prototypes stored in the memory bank to achieve the task of long-distance frame prediction. Besides, a feature discrimination module is adopted to extract the representative dynamic features of various normal events meanwhile preserving the diversity of normal patterns. Experimental results on four public datasets demonstrate the superiority of our method.","doi":"10.24963/ijcai.2024/96","url":"https://ijcai.org/proceedings/2024/96","pdf":"https://ijcai.org/proceedings/2024/0096.pdf","markdown":"https://ijcai.org/proceedings/2024/96.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/96","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":96,"pages":{"first":866,"last":874},"keywords":["Computer Vision: CV: Video analysis and understanding","Computer Vision: CV: Multimodal learning","Machine Learning: ML: Unsupervised learning"],"videos":[],"references":[{"position":1,"raw":"[Cai et al., 2021] Ruichu Cai, Hao Zhang, Wen Liu, Shenghua Gao, and Zhifeng Hao. 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