Abnormal event detection in videos using binary features

In this paper we address the problem of online video abnormal event detection. A vast number of methods to automatically detect abnormal events in videos have been recently proposed. However, the majority of these recently proposed methods cannot attain online performance; in other words, they cannot detect events as soon as they occur. Thus there is a lack of methods specifically aimed to detect events in online fashion. In this paper, we propose to incorporate binary features to detect abnormal events in an online manner. This is based on the fact that binary features are well known to require short processing times, compared to double-precision features. The main contribution of this work is then at the feature extraction step. Our experiment results of our binary-based framework show that our proposed binary features help to reduce processing times for anomaly detection, while outperforming other online methods, in terms of detection accuracy.