Abnormal behavior detection in video by using convolutional neural network

Unusual behavior detection is critically important for visual surveillance. It is also a challenging research topic in computer vision. Although much effort has been devoted to tackle this problem, such detection task in a realistic and uncontrolled environment is still far from mature. The major difficulty lies in the ambiguous characteristic in differentiating normal and abnormal behaviors, whose definitions often vary according to the context of video's history. In this paper we propose a framework for detecting and locating abnormal activities in video sequences. The key aspect of our method is the pairing of the 2D and 3D spatial-temporal Convolutional Neural Networks (CNN) for anomaly detection in contiguous video frames. The Features from Accelerated Segment Test (FAST) detector has been used In order to increase the reliability in identifying the interest locations in entry frames of convolutional neural network model. These feature extracted only from volumes of moving pixels that reduce the computational costs. The architecture of CNN model allows us to extract spatial-temporal features that contain complicated motion features. We test our framework on popular benchmark dataset containing various human abnormal activities and situations. Evaluation results show that our method outperforms most of other methods and achieves a very competitive detection performance compared to state-of-the-art methods.