  Multiple-instance learning (MIL) provides an effective way to tackle the
video anomaly detection problem by modeling it as a weakly supervised problem
as the labels are usually only available at the video level while missing for
frames due to expensive labeling cost. We propose to conduct novel Bayesian
non-parametric submodular video partition (BN-SVP) to significantly improve MIL
model training that can offer a highly reliable solution for robust anomaly
detection in practical settings that include outlier segments or multiple types
of abnormal events. BN-SVP essentially performs dynamic non-parametric
hierarchical clustering with an enhanced self-transition that groups segments
in a video into temporally consistent and semantically coherent hidden states
that can be naturally interpreted as scenes. Each segment is assumed to be
generated through a non-parametric mixture process that allows variations of
segments within the same scenes to accommodate the dynamic and noisy nature of
many real-world surveillance videos. The scene and mixture component assignment
of BN-SVP also induces a pairwise similarity among segments, resulting in
non-parametric construction of a submodular set function. Integrating this
function with an MIL loss effectively exposes the model to a diverse set of
potentially positive instances to improve its training. A greedy algorithm is
developed to optimize the submodular function and support efficient model
training. Our theoretical analysis ensures a strong performance guarantee of
the proposed algorithm. The effectiveness of the proposed approach is
demonstrated over multiple real-world anomaly video datasets with robust
detection performance.
