Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video

TL;DR

This paper presents an object-centric auto-encoder framework combined with a one-vs-rest classification approach for abnormal event detection in videos, achieving superior results across multiple benchmarks.

Contribution

It introduces a novel unsupervised feature learning method with object-centric auto-encoders and a supervised clustering-based classifier for improved abnormal event detection.

Findings

Achieved 8.4% higher frame-level AUC on ShanghaiTech dataset.

Outperformed state-of-the-art methods on four benchmark datasets.

Effective separation of normal and abnormal events using dummy anomalies.

Abstract

Abnormal event detection in video is a challenging vision problem. Most existing approaches formulate abnormal event detection as an outlier detection task, due to the scarcity of anomalous data during training. Because of the lack of prior information regarding abnormal events, these methods are not fully-equipped to differentiate between normal and abnormal events. In this work, we formalize abnormal event detection as a one-versus-rest binary classification problem. Our contribution is two-fold. First, we introduce an unsupervised feature learning framework based on object-centric convolutional auto-encoders to encode both motion and appearance information. Second, we propose a supervised classification approach based on clustering the training samples into normality clusters. A one-versus-rest abnormal event classifier is then employed to separate each normality cluster from the rest. For the purpose of training the classifier, the other clusters act as dummy anomalies. During inference, an object is labeled as abnormal if the highest classification score assigned by the one-versus-rest classifiers is negative. Comprehensive experiments are performed on four benchmarks: Avenue, ShanghaiTech, UCSD and UMN. Our approach provides superior results on all four data sets. On the large-scale ShanghaiTech data set, our method provides an absolute gain of 8.4% in terms of frame-level AUC compared to the state-of-the-art method [Sultani et al., CVPR 2018].