Reviewing Approaches and Techniques for Detecting Suspicious Human Behavior: A Comprehensive Survey

Abstract

The paper aims to review related works that focus on detecting suspicious human behavior using machine-learning techniques. Suspicious human behavior refers to behaviors that may indicate involvement in or preparation for a crime. Detecting such behaviors before a crime is committed allows law enforcement to take early action and prevent criminal activities. One of the challenges in developing an effective detection system for suspicious human behavior is the absence of a well-defined definition for such behaviors. Different definitions can lead to various methods for designing the detection system. The paper explores different definitions and their implications on the design of detection systems and mentions two types of methods that can be used for detecting suspicious human behavior: image-based methods and saliency mapping. Image-based methods utilize image or video recognition techniques to analyze objects held by individuals or recognize specific activities. Saliency mapping, on the other hand, focuses on emphasizing the movement of individuals using techniques like optical flow calculation to generate saliency maps. Additionally, the paper highlights the increasing popularity of embedded machine learning, particularly on portable platforms. The use of embedded machine learning allows for the deployment of machine learning models on mobile or lightweight devices. This can be relevant for developing efficient and portable systems for detecting suspicious human behavior. Overall, the paper aims to provide an overview of existing works in the field of suspicious human behavior detection using machine learning, exploring different definitions and methods employed in the literature.