Bottom-Up/Top-Down Coordination in a MultiAgent Visual Sensor Network * F. Castanedo, M.A. Patricio, J. Garc´ıa and J.M. Molina University Carlos III of Madrid Computer Science Department Applied Artificial Intelligence Group Avda. Universidad Carlos III 22, 28270-Colmenarejo (Madrid) {fcastane, mpatrici, jgherrer}@inf.uc3m.es, molina@ia.uc3m.es Abstract In this paper an approach for multi-sensor coordination in a multiagent visual sensor network is presented. A Belief- Desire-Intention model of multiagent systems is employed. In this multiagent system, the interactions between several surveillance-sensor agents and their respective fusion agent are discussed. The surveillance process is improved using a bottom-up/top-down coordination approach, in which a fu- sion agent controls the coordination process. In the bottom- up phase the information is sent to the fusion agent. On the other hand, in the top-down stage, feedback messages are sent to those surveillance-sensor agents that are performing an inconsistency tracking process with regard to the global fused tracking process. This feedback information allows to the surveillance-sensor agent to correct its tracking pro- cess. Finally, preliminary experiments with the PETS 2006 database are presented. 1. Introduction A multiagent visual sensor network is a distributed network of several intelligent software agents with visual capabili- ties [1]. An intelligent software agent is a computational process that has several characteristics [2]: (1) ”reactivity” (allowing agents to perceive and respond to a changing en- vironment), (2) ”social ability” (by which agents interact with other agents) and (3) ”proactiveness” (through which agents behave in a goal-directed fashion). Wooldridge and Jennings give a strong notion of agent which also uses mental components such as belief, desire and intentions (BDI).The BDI model is one of the best known and studied models of practical reasoning [3]. It is based on a philo- sophical model of human practical reasoning, originally de- veloped by M. Bratman [4] and reduces the explanation for complex human behavior to a motivational stance [5]. This means that the causes for actions are always related to the * Funded by projects Ministerio de Fomento (SINPROB), CICYT TEC2005-07186 and CAM MADRINET S-0505/TIC/0255 human desires ignoring other facets of human motivations to act. And finally, it also uses, in a consistent way, psycho- logical concepts that closely correspond to the terms that humans often use to explain their behavior. In a visual sensor network, the integration of the results obtained from multiple visual sensors can provide more ac- curate information than using a single visual sensor [6] [7]. This allows, for example, a improved tracking accuracy in a surveillance system. However, data fusion must be per- formed with due care, because even though multiple visual sensors provide more information of the same object; this information could be inconsistent between them. A lot of reasons could provide inconsistent or wrong information in a visual sensor network when objects are being tracked. First, the object could be affected by shadow [8] when it is being tracked. This shadow could be originated by external conditions. Second, external conditions could affect the ac- curacy of the tracking process. For example: changes in il- lumination conditions, a sudden increment in wind velocity, all of them affect to the foreground detector and therefore the global tracking process. Another problem which a mul- tiagent visual sensor network must take into account are the partial occlusions of the objects that are being tracked [1]. In our proposed visual sensor system the data fusion process is carried out by a fusion agent in the multiagent visual sensor network. The fusion agent informs to each surveillance-sensor agent which are being performing an in- consistent tracking. The main objective of our approach is to coordinate the network of visual sensors, and the fusion agent is the manager of this coordination process. This paper focus on the interactions between several surveillance-sensor agents and their respective fusion agent in order to solve the specific problems of inconsistencies. In the next section the related work using multiagent systems in a visual sensor network is reviewed and our multiagent approach is presented. Later, we further explain the bottom- up/top-down coordination in a multiagent visual sensor net- work. Then we present experimental results of the proposed method and finally the conclusions of this research. 1 978-1-4244-1696-7/07/$25.00 ©2007 IEEE.