Abstract
Because of ambiguous definition of anomaly and the complexity of real data, anomaly detection in videos is of utmost importance in intelligent video surveillance. We approach this problem by learning a novel 3D convolution autoencoder architecture to capture informative spatiotemporal representation, and an 2D convolutional autoencoder to learn the pixel-wise correspondences of appearance and motion information to boost the performance. Experiments on some publicly available datasets demonstrate the effectiveness and competitive performance of our method on anomaly detection in videos.
Supported by Wuhan University.
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Acknowledgment
The work is supported by the funding CXFW-18-413100063 of Wuhan University. It is also supported by the Huawei-Wuhan University Funding (No. 250000916) and the National Key Research and Development Program of China (No. 2018YFB1600600).
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Chang, Y., Tu, Z., Luo, B., Qin, Q. (2020). Learning Spatiotemporal Representation Based on 3D Autoencoder for Anomaly Detection. In: Cree, M., Huang, F., Yuan, J., Yan, W. (eds) Pattern Recognition. ACPR 2019. Communications in Computer and Information Science, vol 1180. Springer, Singapore. https://doi.org/10.1007/978-981-15-3651-9_17
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DOI: https://doi.org/10.1007/978-981-15-3651-9_17
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