Abstract
Detecting illegal activities using video anomaly detection is an enormous challenge in security and surveillance. The lack of labeled instances for anomalous actions poses a significant obstacle to existing learning techniques, and determining the optimal data representation that captures the essential features and patterns vital for detecting anomalies proves to be exceedingly difficult. We have developed a few-shot video anomaly detection method, FewVAD, which employs a key-frame selection module and spatial-temporal relational modeling to extract pertinent features and reduce temporal redundancy from lengthy surveillance recordings. We have evaluated our method on two popular surveillance datasets, UCF-Crime and XD-Violence, and compared its performance against established few-shot models and other unsupervised and weakly supervised learning video anomaly detection models. Our model has attained an accuracy of 41.7% and 54.3% for 5-way 5-shot few-shot configuration on the UCF-Crime and XD-Violence datasets, respectively. Furthermore, it has obtained an AUC score of 86.60% for the 2-way anomaly detection task on the UCFCrime dataset. FewVAD achieves a milestone in few-shot video anomaly detection, competing strongly with current weakly-supervised and unsupervised VAD methods.
Original language | English |
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Title of host publication | AVSS 2024 - 20th IEEE International Conference on Advanced Video and Signal-Based Surveillance |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Edition | 2024 |
ISBN (Electronic) | 9798350374285 |
DOIs | |
State | Published - 2024 |
Event | 20th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2024 - Niagara Falls, Canada Duration: 15 Jul 2024 → 16 Jul 2024 |
Conference
Conference | 20th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2024 |
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Country/Territory | Canada |
City | Niagara Falls |
Period | 15/07/24 → 16/07/24 |