Imitation-Based Active Camera Control with Deep Convolutional Neural Network

TL;DR

This paper introduces a deep learning-based active camera control system that directly maps visual input to camera movements, improving multi-target monitoring efficiency and robustness in surveillance scenarios.

Contribution

It presents an end-to-end deep convolutional neural network approach for active camera control, bypassing traditional modular pipelines and enabling real-time multi-target tracking.

Findings

Achieves up to 25 FPS in monitoring tasks.

Outperforms traditional methods in target count and monitoring duration.

Demonstrates robustness under varying conditions.

Abstract

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.