A hierarchical neuro-fuzzy architecture for human behavior analysis

Analysis and detection of human behaviors from video sequences has became recently a very hot research topic in computer vision and artificial intelligence. Indeed, human behavior understanding plays a fundamental role in several innovative application domains such as smart video surveillance, ambient intelligence and content-based video information retrieval. However, the uncertainty and vagueness that typically characterize human daily activities make frameworks for human behavior analysis (HBA) hard to design and develop. In order to bridge this gap, this paper proposes a hierarchical architecture, based on a tracking algorithm, time-delay neural networks and fuzzy inference systems, aimed at improving the performance of current HBA systems in terms of scalability, robustness and effectiveness in behavior detection. Precisely, the joint use of the aforementioned methodologies enables both a quantitative and qualitative behavioral analysis that efficiently face the intrinsic people/objects tracking imprecision and provide context aware and semantic capabilities for better identifying a given activity. The validity and effectiveness of the proposed framework have been verified by using the well-known CAVIAR dataset and comparing our system’s performance with other similar approaches working on the same dataset.