Object-Aware Skeleton-Based Anomaly Detection in Surveillance Videos
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- MORIYAMA Ryo
- 青山学院大学大学院
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- KANEKO Naoshi
- 東京電機大学
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- SUMI Kazuhiko
- 青山学院大学
Bibliographic Information
- Other Title
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- 監視カメラ映像における物体情報を付与した骨格ベースの異常行動検知
Abstract
<p>This paper proposes an object-aware skeleton-based anomaly detection method for surveillance videos. The previous skeleton-based anomaly detection approaches learn to reconstruct normal skeleton patterns solely from the skeleton information. However, such methods suffer from detecting object-related abnormal behavior, which has a similar skeleton pose to normal behavior (e.g., riding bicycles/motorcycles). To improve the detection accuracy of such anomalies, we propose incorporating the information of objects (bounding boxes and class labels) around humans. The object and skeleton information are jointly processed through an encoder-decoder RNN to reconstruct the information. We evaluate the proposed method on the HR-ShanghaiTech dataset and achieve an accuracy improvement of 3.1%, reaching 78.2% in the best model.</p>
Journal
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- Journal of the Japan Society for Precision Engineering
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Journal of the Japan Society for Precision Engineering 89 (12), 934-941, 2023-12-05
The Japan Society for Precision Engineering
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Keywords
Details 詳細情報について
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- CRID
- 1390016880936759168
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- ISSN
- 1882675X
- 09120289
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- Text Lang
- ja
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- Data Source
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- JaLC
- Crossref
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- Abstract License Flag
- Disallowed