Real-world Video Anomaly Detection by Extracting Salient Features in Videos

We propose a lightweight and accurate method for detecting anomalies in videos. Existing methods used multiple-instance learning (MIL) to determine the normal/abnormal status of each segment of the video. Recent successful researches argue that it is important to learn the temporal relationships among segments to achieve high accuracy, instead of focusing on only a single segment. Therefore we analyzed the existing methods that have been successful in recent years, and found that while it is indeed important to learn all segments together, the temporal orders among them are irrelevant to achieving high accuracy. Based on this finding, we do not use the MIL framework, but instead propose a lightweight model with a self-attention mechanism to automatically extract features that are important for determining normal/abnormal from all input segments. As a result, our neural network model has 1.3% of the number of parameters of the existing method. We evaluated the frame-level detection accuracy of our method on three benchmark datasets (UCF-Crime, ShanghaiTech, and XD-Violence) and demonstrate that our method can achieve the comparable or better accuracy than state-of-the-art methods.