Agent-based framework to individual tracking in unconstrained environments

Agents with intelligent perceptual capabilities are considered state-of-the-art in advanced intelligent systems. In addition, a multiagent system is considered an enabling technology for applications that rely on distributed and parallel processing, including data, information and knowledge in complex computing environments. With the aim of creating advanced intelligent systems with visual perception, this paper presents an agent-based framework to individual tracking in unconstrained environments. The framework has three types of agents that interact using the Contract Net Protocol. The face detector and tracker agents perform fully automatic single-sample face recognition using the Viola-Jones and the Scale Invariant Feature Transform/Speeded Up Robust Features algorithms. The experimental results show that the framework adequately recognizes and tracks individuals in unconstrained environments, indicating the path the individuals have taken and the time they spent in the field of view of the surveillance agents. Some of the open source framework advantages are the distribution in heterogeneous infrastructures, the expansion with new agents using different face recognition algorithms (e.g., Eigenfaces), and the individual tracking logs that can be used in different ways, e.g., improve security in surveillance areas such as automated teller machines, self-paying kiosks, movie box offices, and malls.