  Anomaly detection in videos is a significant yet challenging problem.
Previous approaches based on deep neural networks employ either
reconstruction-based or prediction-based approaches. Nevertheless, existing
reconstruction-based methods 1) rely on old-fashioned convolutional
autoencoders and are poor at modeling temporal dependency; 2) are prone to
overfit the training samples, leading to indistinguishable reconstruction
errors of normal and abnormal frames during the inference phase. To address
such issues, firstly, we get inspiration from transformer and propose ${\textbf
S}$patio-${\textbf T}$emporal ${\textbf A}$uto-${\textbf T}$rans-${\textbf
E}$ncoder, dubbed as $\textbf{STATE}$, as a new autoencoder model for enhanced
consecutive frame reconstruction. Our STATE is equipped with a specifically
designed learnable convolutional attention module for efficient temporal
learning and reasoning. Secondly, we put forward a novel reconstruction-based
input perturbation technique during testing to further differentiate anomalous
frames. With the same perturbation magnitude, the testing reconstruction error
of the normal frames lowers more than that of the abnormal frames, which
contributes to mitigating the overfitting problem of reconstruction. Owing to
the high relevance of the frame abnormality and the objects in the frame, we
conduct object-level reconstruction using both the raw frame and the
corresponding optical flow patches. Finally, the anomaly score is designed
based on the combination of the raw and motion reconstruction errors using
perturbed inputs. Extensive experiments on benchmark video anomaly detection
datasets demonstrate that our approach outperforms previous
reconstruction-based methods by a notable margin, and achieves state-of-the-art
anomaly detection performance consistently. The code is available at
https://github.com/wyzjack/MRMGA4VAD.
