  Video anomaly detection is recently formulated as a multiple instance
learning task under weak supervision, in which each video is treated as a bag
of snippets to be determined whether contains anomalies. Previous efforts
mainly focus on the discrimination of the snippet itself without modeling the
temporal dynamics, which refers to the variation of adjacent snippets.
Therefore, we propose a Discriminative Dynamics Learning (DDL) method with two
objective functions, i.e., dynamics ranking loss and dynamics alignment loss.
The former aims to enlarge the score dynamics gap between positive and negative
bags while the latter performs temporal alignment of the feature dynamics and
score dynamics within the bag. Moreover, a Locality-aware Attention Network
(LA-Net) is constructed to capture global correlations and re-calibrate the
location preference across snippets, followed by a multilayer perceptron with
causal convolution to obtain anomaly scores. Experimental results show that our
method achieves significant improvements on two challenging benchmarks, i.e.,
UCF-Crime and XD-Violence.
