[Lecture Notes in Computer Science] Pattern Recognition and Computer Vision Volume 11259 (First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part IV) || Asymmetric Two-Stream Networks for RGB-Disparity Based Object Detection

摘要： Currently, most methods of object detection are monocular-based. However, due to the sensitivity to color, these methods can not handle many hard samples. With the depth information, disparity maps are helpful to get over this problem. In this paper, we propose the asymmetric two-stream networks for RGB-Disparity based object detection. Our method consists of two networks, Disparity Representations Mining Network (DRMN) and Muti-Modal Detection Network (MMDN), to combine RGB and disparity data for more accurate detection. Unlike normal two-stream networks, our model is asymmetric because of the di?erent capacity of RGB and disparity data. We are the ?rst to propose a deep learning based framework utilizing only binocular information for object detection. The experiment results on KITTI and our proposed BPD dataset demonstrate that our method can achieve a signi?cant increase in performance e?ciently and get the state-of-the-art.