Appearance and motion based deep learning architecture for moving object detection in moving camera

Byeongho Heo, Kimin Yun, Jin Young Choi

Abstract: Background subtraction from the given image is a widely used method for moving object detection. However, this method is vulnerable to dynamic background in a moving camera video. In this paper, we propose a novel moving object detection approach using deep learning to achieve a robust performance even in a dynamic background. The proposed approach considers appearance features as well as motion features. To this end, we design a deep learning architecture composed of two networks: an appearance network and a motion network. The two networks are combined to detect moving object robustly to the background motion by utilizing the appearance of the target object in addition to the motion difference. In the experiment, it is shown that the proposed method achieves 50 fps speed in GPU and outperforms state-of-the-art methods for various moving camera videos.