A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video

Authors

Mariana Iuliana Georgescu, University of Bucharest, SecurifAI and the Department of Computer Science, Bucuresti, 030018, Romania
Radu Tudor Ionescu, University of Bucharest, SecurifAI and the Department of Computer Science, Bucuresti, 030018, Romania & Romanian Young Academy, Bucharest, 050663, Romania
Fahad Shahbaz Khan, Mohamed bin Zayed University of Artificial IntelligenceFollow
Marius Popescu, University of Bucharest, SecurifAI and the Department of Computer Science, Bucuresti, 030018, Romania
Mubarak Shah, University of Central Florida, Center for Research in Computer Vision (CRCV), Department of Computer Science, Orlando, 32816, FL, United States

Document Type

Article

Publication Title

IEEE Transactions on Pattern Analysis and Machine Intelligence

Abstract

Abnormal event detection in video is a complex computer vision problem that has attracted significant attention in recent years. The complexity of the task arises from the commonly-adopted definition of an abnormal event, that is, a rarely occurring event that typically depends on the surrounding context. Following the standard formulation of abnormal event detection as outlier detection, we propose a background-agnostic framework that learns from training videos containing only normal events. Our framework is composed of an object detector, a set of appearance and motion auto-encoders, and a set of classifiers. Since our framework only looks at object detections, it can be applied to different scenes, provided that normal events are defined identically across scenes and that the single main factor of variation is the background. This makes our method background agnostic, as we rely strictly on objects that can cause anomalies, and not on the background. To overcome the lack of abnormal data during training, we propose an adversarial learning strategy for the auto-encoders. We create a scene-agnostic set of out-of-domain pseudo-abnormal examples, which are correctly reconstructed by the auto-encoders before applying gradient ascent on the pseudo-abnormal examples. We further utilize the pseudo-abnormal examples to serve as abnormal examples when training appearance-based and motion-based binary classifiers to discriminate between normal and abnormal latent features and reconstructions. Furthermore, to ensure that the auto-encoders focus only on the main object inside each bounding box image, we introduce a branch that learns to segment the main object. We compare our framework with the state-of-the-art methods on four benchmark data sets, using various evaluation metrics. Compared to existing methods, the empirical results indicate that our approach achieves favorable performance on all data sets. In addition, we provide region-based and track-based annotations for two large-scale abnormal event detection data sets from the literature, namely ShanghaiTech and Subway. © 1979-2012 IEEE.

First Page

4505

Last Page

4523

DOI

10.1109/TPAMI.2021.3074805

Publication Date

9-1-2022

Keywords

Abnormal event detection, adversarial training, anomaly detection, auto-encoders, security and surveillance, Object detection, Signal encoding

Comments

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Recommended Citation

M. I. Georgescu, R. T. Ionescu, F. S. Khan, M. Popescu and M. Shah, "A Background-Agnostic Framework With Adversarial Training for Abnormal Event Detection in Video," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 9, pp. 4505-4523, 1 Sept. 2022, doi: 10.1109/TPAMI.2021.3074805.