Anomaly Detection Using Prediction Error with Spatio-Temporal Convolutional LSTM
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Từ khóa:
Convolutional LSTM
convolutional autoencoder
prediction error
reconstruction error
anomaly detection
Tóm tắt
In this paper, we propose a novel method for video anomaly detection motivated by an existing architecture for sequence-to-sequence prediction and reconstruction using a spatio-temporal convolutional Long Short-Term Memory (convLSTM). As in previous work on anomaly detection, anomalies arise as spatially localised failures in reconstruction or prediction. In experiments with five benchmark datasets, we show that using prediction gives superior performance to using reconstruction. We also compare performance with different length input/output sequences. Overall, our results using prediction are comparable with the state of the art on the benchmark datasets.
Tài liệu tham khảo
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Jun 30, 2022
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Cách trích dẫn
Tran, H. T. M. ., và D. Hogg. “Anomaly Detection Using Prediction Error With Spatio-Temporal Convolutional LSTM”. Tạp Chí Khoa học Và Công nghệ - Đại học Đà Nẵng, vol 20, số p.h 6.2, Tháng Sáu 2022, tr 7-12, doi:10.31130/ud-jst.2022.289E.
