| Autor: |
Arpit Bajgoti, Rishik Gupta, Prasanalakshmi Balaji, Rinky Dwivedi, Meena Siwach, Deepak Gupta |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 11, Pp 111093-111105 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2023.3321801 |
| Popis: |
Detecting anomalous events in videos is a challenging task due to their infrequent and unpredictable nature in real-world scenarios. In this paper, we propose SwinAnomaly, a video anomaly detection approach based on a conditional GAN-based autoencoder with feature extractors based on Swin Transformers. Our approach encodes spatiotemporal features from a sequence of video frames using a 3D encoder and upsamples them to predict a future frame using a 2D decoder. We utilize patch-wise mean squared error and Simple Online and Real-time Tracking (SORT) for real-time anomaly detection and tracking. Our approach outperforms existing prediction-based video anomaly detection methods and offers flexibility in localizing anomalies through several parameters. Extensive testing shows that SwinAnomaly achieves state-of-the-art performance on public benchmarks, demonstrating the effectiveness of our approach for real-world video anomaly detection. Furthermore, our proposed approach has the potential to enhance public safety and security in various applications, including crowd surveillance, traffic monitoring, and industrial safety. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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