| Title | A memory-enhanced anomaly detection method for surveillance videos |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Zhang, Lixue, Li, Yuqin, Gao, Yan, Li, Yanfang, Shi, Weili, Jiang, Zhengang |
| Conference Name | 2021 International Conference on Electronic Information Engineering and Computer Science (EIECS) |
| Date Published | sep |
| Keywords | anomaly detection, Benchmark testing, diversity reception, feature extraction, Human Behavior, Memory modules, memory-enhanced module, Metrics, Prototypes, pubcrawl, resilience, Resiliency, Robustness, surveillance, Surveillance video, video surveillance |
| Abstract | Surveillance videos can capture anomalies in real scenarios and play an important role in security systems. Anomaly events are unpredictable, which reflect the unsupervised nature of the problem. In addition, it is difficult to construct a complete video dataset which contains all normal events. Based on the diversity of normal events, this paper proposes a memory-enhanced unsupervised method for anomaly detection. The proposed method reconstructs video events by combining prototype features and encoded features to detect anomaly events. Furthermore, a memory module is introduced to better store the prototype patterns of normal events. Experimental results in various benchmark datasets demonstrate the effectiveness and robustness of the proposed method. |
| DOI | 10.1109/EIECS53707.2021.9587995 |
| Citation Key | zhang_memory-enhanced_2021 |
A memory-enhanced anomaly detection method for surveillance videos