  The increasing need for automated visual monitoring and control for
applications such as smart camera surveillance, traffic monitoring, and
intelligent environments, necessitates the improvement of methods for visual
active monitoring. Traditionally, the active monitoring task has been handled
through a pipeline of modules such as detection, filtering, and control. In
this paper we frame active visual monitoring as an imitation learning problem
to be solved in a supervised manner using deep learning, to go directly from
visual information to camera movement in order to provide a satisfactory
solution by combining computer vision and control. A deep convolutional neural
network is trained end-to-end as the camera controller that learns the entire
processing pipeline needed to control a camera to follow multiple targets and
also estimate their density from a single image. Experimental results indicate
that the proposed solution is robust to varying conditions and is able to
achieve better monitoring performance both in terms of number of targets
monitored as well as in monitoring time than traditional approaches, while
reaching up to 25 FPS. Thus making it a practical and affordable solution for
multi-target active monitoring in surveillance and smart-environment
applications.
