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

Local Anomaly Detection in Videos using Object-Centric Adversarial Learning