  The need for automated real-time visual systems in applications such as smart
camera surveillance, smart environments, and drones necessitates the
improvement of methods for visual active monitoring and control. Traditionally,
the active monitoring task has been handled through a pipeline of modules such
as detection, filtering, and control. However, such methods are difficult to
jointly optimize and tune their various parameters for real-time processing in
resource constraint systems. In this paper a deep Convolutional Camera
Controller Neural Network is proposed to go directly from visual information to
camera movement to provide an efficient solution to the active vision problem.
It is trained end-to-end without bounding box annotations to control a camera
and follow multiple targets from raw pixel values. Evaluation through both a
simulation framework and real experimental setup, indicate that the proposed
solution is robust to varying conditions and able to achieve better monitoring
performance than traditional approaches both in terms of number of targets
monitored as well as in effective monitoring time. The advantage of the
proposed approach is that it is computationally less demanding and can run at
over 10 FPS (~4x speedup) on an embedded smart camera providing a practical and
affordable solution to real-time active monitoring.
