Using observations to recognize the behavior of interacting multi-agent systems

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dc.contributor.author Feldman, Adam Michael en_US
dc.date.accessioned 2008-09-17T19:36:08Z
dc.date.available 2008-09-17T19:36:08Z
dc.date.issued 2008-05-19 en_US
dc.identifier.uri http://hdl.handle.net/1853/24771
dc.description.abstract Behavioral research involves the study of the behaviors of one or more agents (often animals) in order to better understand the agents' thoughts and actions. Identifying subject movements and behaviors based upon those movements is a critical, time-consuming step in behavioral research. To successfully perform behavior analysis, three goals must be met. First, the agents of interest are observed, and their movements recorded. Second, each individual must be uniquely identified. Finally, behaviors must be identified and recognized. I explore a system that can uniquely identify and track agents, then use these tracks to automatically build behavioral models and recognize similar behaviors in the future. I address the tracking and identification problems using a combination of laser range finders, active RFID sensors, and probabilistic models for real-time tracking. The laser range component adds environmental flexibility over vision based systems, while the RFID tags help disambiguate individual agents. The probabilistic models are important to target identification during the complex interactions with other agents of similar appearance. In addition to tracking, I present work on automatic methods for generating behavioral models based on supervised learning techniques using the agents' tracked data. These models can be used to classify new tracked data and identify the behavior exhibited by the agent, which can then be used to help automate behavior analysis. en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Behavior recognition en_US
dc.subject Multi-target tracking en_US
dc.subject.lcsh Behavioral assessment
dc.subject.lcsh Automatic data collection systems
dc.subject.lcsh Automatic tracking
dc.title Using observations to recognize the behavior of interacting multi-agent systems en_US
dc.type Dissertation en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Computing en_US
dc.description.advisor Committee Chair: Balch, Tucker; Committee Member: Essa, Irfan; Committee Member: Isbell, Charles; Committee Member: Starner, Thad; Committee Member: Wallen, Kim en_US


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