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

C^3Net: End-to-End deep learning for efficient real-time visual active camera control