Recognizing multi-view objects with occlusions using a deep architecture

Image-based object recognition is employed widely in many computer vision applications such as image semantic annotation and object location. However, traditional object recognition algorithms based on the 2D features of RGB data have difficulty when objects overlap and image occlusion occurs. At present, RGB-D cameras are being used more widely and the RGB-D depth data can provide auxiliary information to address these challenges. In this study, we propose a deep learning approach for the efficient recognition of 3D objects with occlusion. First, this approach constructs a multi-view shape model based on 3D objects by using an encode-decode deep learning network to represent the features. Next, 3D object recognition in indoor scenes is performed using random forests. The application of deep learning to RGB-D data is beneficial for recovering missing information due to image occlusion. Our experimental results demonstrate that this approach can significantly improve the efficiency of feature representation and the performance of object recognition with occlusion.