Human Behavior Recognition in Shopping Settings

This paper presents a new application that improves communication between digital media and customers at a point of sale. The system uses several methods from various areas of computer vision such as motion detection, object tracking, behavior analysis and recognition, semantic description of behavior, and scenario recognition. Specifically, the system is divided in three parts: low-level, mid-level, and high-level analysis. Low-level analysis detects and tracks moving object in the scene. Then mid-level analysis describes and recognizes behavior of the tracked objects. Finally high-level analysis produces a semantic interpretation of the detected behavior and recognizes predefined scenarios. Our research is developed in order to build a real-time application that recognizes human behaviors while shopping. Specifically, the system detects customer interests and interactions with various products at a point of sale.