Detecting abnormal behaviors in surveillance videos based on fuzzy clustering and multiple Auto-Encoders

In this paper, we present a novel framework to detect abnormal behaviors in surveillance videos by using fuzzy clustering and multiple Auto-Encoders (FMAE). As detecting abnormal behaviors is often treated as an unsupervised task, how to describe normal patterns becomes the key point. Considering there are many types of normal behaviors in the daily life, we use the fuzzy clustering technique to roughly divide the training samples into several clusters so that each cluster stands for a normal pattern. Then we deploy multiple Auto-Encoders to estimate these different types of normal behaviors from weighted samples. When testing on an unknown video, our framework can predict whether it contains abnormal behaviors or not by summarizing the reconstruction cost through each Auto-Encoder. Since there are always lots of redundancies in the surveillance video, Auto-Encoder is a pretty good tool to capture common structures of normal video sequences automatically as well as estimate normal patterns. The experimental results show that our approach achieves good performance on three public video analysis datasets and statistically outperforms the state-of-the-art approaches under some scenes.