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Erschienen in:
Spatial-Temporal Transformer with Error-Restricted Variance Estimation for Time Series Anomaly Detection Kapitel lesen Erstes Kapitel lesen

2024 | OriginalPaper | Buchkapitel

SATJiP: Spatial and Augmented Temporal Jigsaw Puzzles for Video Anomaly Detection

verfasst von : Liheng Shen, Tetsu Matsukawa, Einoshin Suzuki

Erschienen in: Advances in Knowledge Discovery and Data Mining

Verlag: Springer Nature Singapore

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Abstract

Video Anomaly Detection (VAD) is a significant task, which refers to taking a video clip as input and outputting class labels, e.g., normal or abnormal, at the frame level. Wang et al. proposed a method called DSTJiP, which trains the model by solving Decoupled Spatial and Temporal Jigsaw Puzzles and achieves impressive VAD performance. However, the model sometimes fails to detect abnormal human actions where abnormal motions are accompanied by normal motions. The reason is that the model learns representations of little- and non-motion parts of training examples, resulting in being insensitive to abnormal motions. To circumvent this problem, we propose to solve Spatial and Augmented Temporal Jigsaw Puzzles (SATJiP) as an extension of DSTJiP. SATJiP encourages the model to focus on motions by a novel pretext task, enabling it to detect abnormal motions accompanied by normal motions. Experiments conducted on three standard VAD benchmarks demonstrate that SATJiP outperforms the state-of-the-art methods.

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Vorheriges Kapitel Multi-task Contrastive Learning for Anomaly Detection on Attributed Networks
Nächstes Kapitel STL-ConvTransformer: Series Decomposition and Convolution-Infused Transformer Architecture in Multivariate Time Series Anomaly Detection
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Metadaten
Titel
SATJiP: Spatial and Augmented Temporal Jigsaw Puzzles for Video Anomaly Detection
verfasst von
Liheng Shen
Tetsu Matsukawa
Einoshin Suzuki
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-981-9722-41-9

Electronic ISBN: 978-981-9722-42-6

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-981-97-2242-6_3

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