Abstract

Human Abnormal Behavior Detection is needed in several important sectors, especially in the field of public safety. Video Surveillance Systems are a trigger in detecting strange human behavior. However, this abnormal behavior data is rare and requires more costs to obtain, so the unsupervised learning method is used to study normal human behavior patterns only. There have been several previous studies that have provided significant results using this technique. There have been several previous survey studies related to human abnormal behavior detection, but they did not focus on unsupervised learning methods and the scope was too broad. In this paper, I present a survey regarding human abnormal behavior detection in more depth conceptually and classify it into two large parts, such as reconstruction-based detection and generative-based detection. This paper also offers a more extensive comparison with several popular datasets that are often used in previous research. Finally, I also shared several challenges and open research issues that emerged from our survey regarding future directions for future development.