Local Anomaly Detection in Videos using Object-Centric Adversarial Learning
This addresses the problem of detecting anomalous events in surveillance videos for security applications, representing an incremental improvement over existing methods.
The paper tackles unsupervised local anomaly detection in videos by proposing a two-stage object-centric adversarial framework that uses only object regions. The method achieves competitive or superior results on four public benchmarks (UMN, UCSD, Avenue, ShanghaiTech) compared to state-of-the-art approaches.
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.