Asymmetric Two-Stream Networks for RGB-Disparity Based Object Detection
Currently,most methods of object detection are monocularbased.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 different capacity of RGB and disparity data.We are the first 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 significant increase in performance efficiently and get the state-of-the-art.
Object detection Two-stream networks RGBD data
Ruizhi Lu Jianhuang Lai Xiaohua Xie
School of Data and Computer Science,Sun Yat-sen University,Guangzhou,China;Guangdong Key Laboratory of Information Security Technology,Guangzhou,China;Key Laboratory of Machine Intelligence and Advanced Computing,Ministry of Education,Beijing,China
国际会议
广州
英文
4-15
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）