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

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. 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. 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.

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Anomaly Detection CUHK Avenue Background-Agnostic Framework AUC 92.3% # 8
RBDC 65.05 # 4
TBDC 66.85 # 6
FPS 25 # 5
Anomaly Detection ShanghaiTech Background-Agnostic Framework AUC 82.7% # 11
Anomaly Detection UBnormal Background-Agnostic Framework AUC 61.3% # 10
RBDC 25.43 # 2
TBDC 56.27 # 2
Anomaly Detection UCSD Ped2 Background-Agnostic AUC 98.7% # 3
FPS 24 # 2
Abnormal Event Detection In Video UCSD Ped2 Background-Agnostic Framework AUC 98.7% # 2
Anomaly Detection In Surveillance Videos UCSD Peds2 Background-Agnostic Framework AUC 98.7 # 1
Anomaly Detection UCSD Peds2 Background-Agnostic Framework AUC 98.7 # 1


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