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Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video by Ionescu, Radu Tudor; Khan, Fahad Shahbaz; Georgescu, Mariana-Iuliana; Shao, Ling is a scholarly article available to read on EtoBox.

What is Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video about?

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,

Author

Ionescu, Radu Tudor; Khan, Fahad Shahbaz; Georgescu, Mariana-Iuliana; Shao, Ling

Published

2018

Language

EN