Dynamic camera configuration learning for high-confidence active object detection

The performance of object detection is closely related to the quality of input images. However, the current image acquisition is purely guided by human visual perception, and such camera imaging process ignores the subsequent application. In this context, detection performance is impacted by imaging configuration and dynamic camera motion. To address the above problems, an active object detection framework is proposed in this paper, which aims to build the bridge between imaging configuration and object detection task. Within the proposed framework, a dynamic camera configuration learning approach is presented based on deep reinforcement learning, where the camera is actively controlled to maximize the detection performance. Through iterated interactions between imaging, control and object detection, the deep gap between perception and cognition in the object detection system is eliminated, and the transformation from physical imaging to purposeful imaging is realized. The effectiveness and advantages of the proposed framework are demonstrated in three dynamic environments.