In the last years, the research effort of the scientific community to study systems for ambient intelligence has been really strong. Usually, the systems developed so far base their analysis on images acquired by automatic cameras. In this paper, we propose a way to develop new smart systems that are able to actively decide both what to see and how to see it. In particular, the main idea is to tune the acquisition parameters on the basis of what the system desires to acquire. The regulation strategy is based on two camera parameters, focus and iris. It aims to identify an optimal sequence of steps to enhance the acquisition quality of an object of interest. To this end, a hierarchy of neural networks has been employed first to select which parameter must be regulated then to adjust it. The proposed solution can be applied to both static and moving cameras. The results show how the proposed technique can be applied to images acquired by a moving camera with zoom capabilities for surveillance purposes. Note to Practitioners-The proposed work presents a new active vision paradigm. While traditional active vision systems control the acquisition only by moving the cameras to keep an object of interest in the field-of-view, the proposed solution extends such a concept by introducing the control of the image creation process. The objective is to keep the objects of interest within the field-of-view and at the same time to acquire it with an optimal quality. This is an interesting feature as it allows to focus the attention only on the areas of interest on which our system is interested in thus improving the performance of the image processing tasks. To achieve such an objective, we propose a neural network hierarchy that is able to find out the optimal configuration of the intrinsic camera parameters (focus and iris) in a fast way. This result is even more important if we think that it can be achieved regardless the dimension and the position of the area of interest inside the image.

Active Tuning of Intrinsic Camera Parameters

MICHELONI, Christian;FORESTI, Gian Luca
2009

Abstract

In the last years, the research effort of the scientific community to study systems for ambient intelligence has been really strong. Usually, the systems developed so far base their analysis on images acquired by automatic cameras. In this paper, we propose a way to develop new smart systems that are able to actively decide both what to see and how to see it. In particular, the main idea is to tune the acquisition parameters on the basis of what the system desires to acquire. The regulation strategy is based on two camera parameters, focus and iris. It aims to identify an optimal sequence of steps to enhance the acquisition quality of an object of interest. To this end, a hierarchy of neural networks has been employed first to select which parameter must be regulated then to adjust it. The proposed solution can be applied to both static and moving cameras. The results show how the proposed technique can be applied to images acquired by a moving camera with zoom capabilities for surveillance purposes. Note to Practitioners-The proposed work presents a new active vision paradigm. While traditional active vision systems control the acquisition only by moving the cameras to keep an object of interest in the field-of-view, the proposed solution extends such a concept by introducing the control of the image creation process. The objective is to keep the objects of interest within the field-of-view and at the same time to acquire it with an optimal quality. This is an interesting feature as it allows to focus the attention only on the areas of interest on which our system is interested in thus improving the performance of the image processing tasks. To achieve such an objective, we propose a neural network hierarchy that is able to find out the optimal configuration of the intrinsic camera parameters (focus and iris) in a fast way. This result is even more important if we think that it can be achieved regardless the dimension and the position of the area of interest inside the image.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11390/878764
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