TY - GEN
T1 - Dual-Scale Temporal Dependency Learning for Unsupervised Video Anomaly Detection
AU - Li, Xu
AU - Wang, Xue
AU - Du, Zexing
AU - Wang, Qing
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Video anomaly detection plays an increasingly crucial role in intelligent surveillance systems. Inspired by previous unsupervised methods, this paper focuses on detecting frame-level anomalies with long-term temporal dependencies. To this end, we propose a dual-scale temporal dependency learning method for video anomaly detection model, which consists of two main modules: a single-frame reconstruction module and a multi-frame feature enhancement module, processed end-to-end without relying on any pre-trained models. To validate the proposed approach, we introduce a new Elevator dataset containing various types of remote temporal dependency anomalies. Experimental results on the self-constructed Elevator dataset and two benchmarks demonstrate the effectiveness of our proposed approach.
AB - Video anomaly detection plays an increasingly crucial role in intelligent surveillance systems. Inspired by previous unsupervised methods, this paper focuses on detecting frame-level anomalies with long-term temporal dependencies. To this end, we propose a dual-scale temporal dependency learning method for video anomaly detection model, which consists of two main modules: a single-frame reconstruction module and a multi-frame feature enhancement module, processed end-to-end without relying on any pre-trained models. To validate the proposed approach, we introduce a new Elevator dataset containing various types of remote temporal dependency anomalies. Experimental results on the self-constructed Elevator dataset and two benchmarks demonstrate the effectiveness of our proposed approach.
KW - Frame reconstruction
KW - Long temporal dependency
KW - Unsupervised learning
KW - Video anomaly detection
UR - https://www.scopus.com/pages/publications/85209578423
U2 - 10.1007/978-981-97-8792-0_20
DO - 10.1007/978-981-97-8792-0_20
M3 - 会议稿件
AN - SCOPUS:85209578423
SN - 9789819787913
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 284
EP - 298
BT - Pattern Recognition and Computer Vision - 7th Chinese Conference, PRCV 2024, Proceedings
A2 - Lin, Zhouchen
A2 - Zha, Hongbin
A2 - Cheng, Ming-Ming
A2 - He, Ran
A2 - Liu, Cheng-Lin
A2 - Ubul, Kurban
A2 - Silamu, Wushouer
A2 - Zhou, Jie
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2024
Y2 - 18 October 2024 through 20 October 2024
ER -