Journal
NEUROCOMPUTING
Volume 444, Issue -, Pages 332-337Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2019.12.148
Keywords
Graph convolutional networks; Anomaly detection; Skeleton
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This paper proposes a prediction network for skeleton based video anomaly detection based on spatial temporal graph convolutional networks, achieving state-of-the-art performance compared to other methods.
This paper focus on analyzing graph connection of human joints for skeleton based video anomaly detection, which is more effective and efficient than those image-level reconstruction based or prediction based methods that may be affected by complex background. Specifically, we propose a spatial temporal graph convolutional networks based prediction network for skeleton based video anomaly detection. In other words, we build a normal graph describing graph connection of joints in normal data, where joints of abnormal events will be outliers of this graph. To our knowledge, this is the first work to apply graph convolutional networks on skeleton-based video anomaly detection. Experiments show that our proposed normal graph achieves the-state-of-art performance, compared to those image-level reconstruction-based or prediction-based methods, as well as RNN based methods upon joints. (c) 2020 Published by Elsevier B.V.
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