Abstract
Abnormal event detection is a non-trivial task in machine learning. The primary reason behind this is that the abnormal class occurs sparsely, and its temporal location may not be available. In this paper, we propose a multiple feature-based approach for CitySCENE challenge-based anomaly detection. For motion and context information, Res3D and Res101 architectures are used. Object-level information is extracted by object detection feature-based pooling. Fusion of three channels above gives relatively high performance on the challenge Test set for the general anomaly task. We also show how our method can be used for temporal localisation of the abnormal activity event in a video.
Original language | English |
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Title of host publication | Proceedings of the 28th ACM International Conference on Multimedia |
Editors | Pradeep K. Atrey, Zhu Li |
Place of Publication | New York NY USA |
Publisher | Association for Computing Machinery (ACM) |
Pages | 4674-4678 |
Number of pages | 5 |
ISBN (Electronic) | 9781450379885 |
DOIs | |
Publication status | Published - 2020 |
Event | ACM International Conference on Multimedia 2020 - Online, United States of America Duration: 12 Oct 2020 → 16 Oct 2020 Conference number: 28th https://dl.acm.org/doi/proceedings/10.1145/3394171 (Proceedings) |
Conference
Conference | ACM International Conference on Multimedia 2020 |
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Abbreviated title | MM 2020 |
Country/Territory | United States of America |
Period | 12/10/20 → 16/10/20 |
Internet address |
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Keywords
- anomaly detection
- CitySCENE
- convolutional neural networks