http://hdl.handle.net/10016/7454 2013-05-26T00:49:48Z 2013-05-26T00:49:48Z Castanedo, Federico García, Jesús Patricio Guisado, Miguel Ángel Molina, José M. http://hdl.handle.net/10016/9328 2013-02-20T12:58:19Z 2010-06-30T22:00:00Z

Title: Data fusion to improve trajectory tracking in a Cooperative Surveillance Multi-Agent Architecture Author(s): Castanedo, Federico; García, Jesús; Patricio Guisado, Miguel Ángel; Molina, José M. Abstract: In this paper we present a Cooperative Surveillance Multi-Agent System (CS-MAS) architecture extended to incorporate dynamic coalition formation. We illustrate specific coalition formation using fusion skills. In this case, the fusion process is divided into two layers: (i) a global layer in the fusion center, which initializes the coalitions and (ii) a local layer within coalitions, where a local fusion agent is dynamically instantiated. There are several types of autonomous agent: surveillance–sensor agents, a fusion center agent, a local fusion agent, interface agents, record agents, planning agents, etc. Autonomous agents differ in their ability to carry out a specific surveillance task. A surveillance–sensor agent controls and manages individual sensors (usually video cameras). It has different capabilities depending on its functional complexity and limitations related to sensor-specific aspects. In the work presented here we add a new autonomous agent, called the local fusion agent, to the CS-MAS architecture, addressing specific problems of on-line sensor alignment, registration, bias removal and data fusion. The local fusion agent is dynamically created by the fusion center agent and involves several surveillance–sensor agents working in a coalition. We show how the inclusion of this new dynamic local fusion agent guarantees that, in a video-surveillance system, objects of interest are successfully tracked across the whole area, assuring continuity and seamless transitions. Description: 13 pages, 12 figures.

2010-06-30T22:00:00Z Castanedo, Federico García, Jesús Patricio Guisado, Miguel Ángel Molina, José M. http://hdl.handle.net/10016/9327 2013-02-20T12:49:18Z 2009-12-31T23:00:00Z

Title: A multi-agent architecture based on the BDI model for data fusion in visual sensor networks Author(s): Castanedo, Federico; García, Jesús; Patricio Guisado, Miguel Ángel; Molina, José M. Abstract: The newest surveillance applications is attempting more complex tasks such as the analysis of the behavior of individuals and crowds. These complex tasks may use a distributed visual sensor network in order to gain coverage and exploit the inherent redundancy of the overlapped field of views. This article, presents a Multi-agent architecture based on the Belief-Desire-Intention (BDI) model for processing the information and fusing the data in a distributed visual sensor network. Instead of exchanging raw images between the agents involved in the visual network, local signal processing is performed and only the key observed features are shared. After a registration or calibration phase, the proposed architecture performs tracking, data fusion and coordination. Using the proposed Multi-agent architecture, we focus on the means of fusing the estimated positions on the ground plane from different agents which are applied to the same object. This fusion process is used for two different purposes: (1) to obtain a continuity in the tracking along the field of view of the cameras involved in the distributed network, (2) to improve the quality of the tracking by means of data fusion techniques, and by discarding non reliable sensors. Experimental results on two different scenarios show that the designed architecture can successfully track an object even when occlusions or sensor’s errors take place. The sensor’s errors are reduced by exploiting the inherent redundancy of a visual sensor network with overlapped field of views. Description: 30 pages, 18 figures.-- Article in press.

2009-12-31T23:00:00Z Cilla, Rodrigo Patricio Guisado, Miguel Ángel García, Jesús Berlanga, Antonio Molina, José M. http://hdl.handle.net/10016/9315 2013-02-20T13:01:21Z 2009-01-31T23:00:00Z

Title: Recognizing human activities from sensors using hidden Markov models constructed by feature selection techniques Author(s): Cilla, Rodrigo; Patricio Guisado, Miguel Ángel; García, Jesús; Berlanga, Antonio; Molina, José M. Abstract: In this paper a method for selecting features for Human Activity Recognition from sensors is presented. Using a large feature set that contains features that may describe the activities to recognize, Best First Search and Genetic Algorithms are employed to select the feature subset that maximizes the accuracy of a Hidden Markov Model generated from the subset. A comparative of the proposed techniques is presented to demonstrate their performance building Hidden Markov Models to classify different human activities using video sensors. Description: 19 pages, 8 figures.-- This article belongs to the Special Issue "Sensor Algorithms".

2009-01-31T23:00:00Z Patricio Guisado, Miguel Ángel Carbó, Javier Pérez Concha, Óscar García, Jesús Molina López, José M. http://hdl.handle.net/10016/7829 2013-02-20T13:03:47Z 2006-12-31T23:00:00Z

Title: Multi-Agent Framework in Visual Sensor Networks Author(s): Patricio Guisado, Miguel Ángel; Carbó, Javier; Pérez Concha, Óscar; García, Jesús; Molina López, José M. Abstract: The recent interest in the surveillance of public, military, and commercial scenarios is increasing the need to develop and deploy intelligent and/or automated distributed visual surveillance systems. Many applications based on distributed resources use the so-called software agent technology. In this paper, a multi-agent framework is applied to coordinate videocamera-based surveillance. The ability to coordinate agents improves the global image and task distribution efficiency. In our proposal, a software agent is embedded in each camera and controls the capture parameters. Then coordination is based on the exchange of high-level messages among agents. Agents use an internal symbolic model to interpret the current situation from the messages from all other agents to improve global coordination. Description: 21 pages, 21 figures.-- Journal special issue on Visual Sensor Networks.

2006-12-31T23:00:00Z