Monitoring human activities in large environments is a challenging problem. Such scenarios impose the use of sensor networks and decentralized processing to achieve scalability in terms of spatial environment and task complexity. This article addresses both algorithmic and architectural aspects of distributed tracking systems. The algorithmic approach pursued is that of sequential Bayesian filtering, whose standard formulation is rewritten in a form suitable for distribution within a three-layered modular architecture. Its non-parametric MonteCarlo implementation provides a suitable framework for tackling dynamic resource allocation: representation size of probabilistic estimates is adapted to conveyed uncertainty, while active environment sampling aims at minimizing uncertainty. Independent tracking agencies with competence on a subset of targets are instantiated and continuously reconfigured by a supervisor process with global environment knowledge. The proposed system is then adaptive to its sensing and computing infrastructure, own performance and environment, providing a scalable solution to the problem of visual monitoring of populated, topologically complex environments
Dynamic Resource Allocation for Probabilistic Tracking in Complex Scenes
Lanz, Oswald
2005-01-01
Abstract
Monitoring human activities in large environments is a challenging problem. Such scenarios impose the use of sensor networks and decentralized processing to achieve scalability in terms of spatial environment and task complexity. This article addresses both algorithmic and architectural aspects of distributed tracking systems. The algorithmic approach pursued is that of sequential Bayesian filtering, whose standard formulation is rewritten in a form suitable for distribution within a three-layered modular architecture. Its non-parametric MonteCarlo implementation provides a suitable framework for tackling dynamic resource allocation: representation size of probabilistic estimates is adapted to conveyed uncertainty, while active environment sampling aims at minimizing uncertainty. Independent tracking agencies with competence on a subset of targets are instantiated and continuously reconfigured by a supervisor process with global environment knowledge. The proposed system is then adaptive to its sensing and computing infrastructure, own performance and environment, providing a scalable solution to the problem of visual monitoring of populated, topologically complex environmentsI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
