  We propose a novel unsupervised approach based on a two-stage object-centric
adversarial framework that only needs object regions for detecting frame-level
local anomalies in videos. The first stage consists in learning the
correspondence between the current appearance and past gradient images of
objects in scenes deemed normal, allowing us to either generate the past
gradient from current appearance or the reverse. The second stage extracts the
partial reconstruction errors between real and generated images (appearance and
past gradient) with normal object behaviour, and trains a discriminator in an
adversarial fashion. In inference mode, we employ the trained image generators
with the adversarially learned binary classifier for outputting region-level
anomaly detection scores. We tested our method on four public benchmarks, UMN,
UCSD, Avenue and ShanghaiTech and our proposed object-centric adversarial
approach yields competitive or even superior results compared to
state-of-the-art methods.
