  In this study, we formulate the task of Video Anomaly Detection as a
probabilistic analysis of object bounding boxes. We hypothesize that the
representation of objects via their bounding boxes only, can be sufficient to
successfully identify anomalous events in a scene. The implied value of this
approach is increased object anonymization, faster model training and fewer
computational resources. This can particularly benefit applications within
video surveillance running on edge devices such as cameras. We design our model
based on human reasoning which lends itself to explaining model output in
human-understandable terms. Meanwhile, the slowest model trains within less
than 7 seconds on a 11th Generation Intel Core i9 Processor. While our approach
constitutes a drastic reduction of problem feature space in comparison with
prior art, we show that this does not result in a reduction in performance: the
results we report are highly competitive on the benchmark datasets CUHK Avenue
and ShanghaiTech, and significantly exceed on the latest State-of-the-Art
results on StreetScene, which has so far proven to be the most challenging VAD
dataset.
