A hierarchical neuro-fuzzy architecture for human behavior analysis
| Autor: | Giovanni Acampora, Mario Vento, Pasquale Foggia, Alessia Saggese |
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| Přispěvatelé: | Acampora, Giovanni, Foggia, Pasquale, Saggese, Alessia, Vento, Mario |
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Hierarchical system
Artificial intelligence Information Systems and Management Time delay neural network Neuro-fuzzy Computer science Inference engine Fuzzy inference system Neuro-fuzzy architecture Context (language use) Target tracking Human behavior computer.software_genre Machine learning Fuzzy logic Modelling Theoretical Computer Science Neuro-Fuzzy Modelling Robustness (computer science) Video information retrieval Behavioral research Fuzzy neural network Trajectories analysis Ambient intelligence Artificial neural network business.industry Behavior understanding Network architecture Social science Neural network Computer Science Applications Uncertainty analysis Behavior understanding Fuzzy inference Control and Systems Engineering Human behavior understanding Neuro-Fuzzy Scalability Computer vision Data mining business computer Security system Semantic Software Time delay Model Hierarchical architecture |
| Popis: | We propose a hierarchical architecture for human behavior analysis.Micro-behaviors are analyzed by a time-delay neural network.Macro-behaviors are analyzed by a fuzzy system.The experiments have been performed on the standard CAVIAR datasets. 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. |
| Databáze: | OpenAIRE |
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