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Spatial hierarchical analysis deep neural network for RGB-D object recognition

Shah, S.A.A. (2020) Spatial hierarchical analysis deep neural network for RGB-D object recognition. Lecture Notes in Computer Science, 11994 . pp. 183-193.

Link to Published Version: https://doi.org/10.1007/978-3-030-39770-8_15
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Abstract

Deep learning based object recognition methods have achieved unprecedented success in the recent years. However, this level of success is yet to be achieved on multimodal RGB-D images. The latter can play an important role in several computer vision and robotics applications. In this paper, we present spatial hierarchical analysis deep neural network, called ShaNet, for RGB-D object recognition. Our network consists of convolutional neural network (CNN) and recurrent neural network (RNNs) to analyse and learn distinctive and translationally invariant features in a hierarchical fashion. Unlike existing methods, which employ pre-trained models or rely on transfer learning, our proposed network is trained from scratch on RGB-D data. The proposed model has been tested on two different publicly available RGB-D datasets including Washington RGB-D and 2D3D object dataset. Our experimental results show that the proposed deep neural network achieves superior performance compared to existing RGB-D object recognition methods.

Item Type: Journal Article
Murdoch Affiliation: Information Technology, Mathematics and Statistics
Publisher: Springer Verlag
Copyright: © 2020 Springer Nature Switzerland AG
URI: http://researchrepository.murdoch.edu.au/id/eprint/55061
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