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Depth Value Pre-Processing for Accurate Transfer Learning based RGB-D Object Recognition

Topics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World applications, Financial Applications, Neural Prostheses and Medical Applications, Neural based Data Mining and Complex Information Processing, Neural Network Software and Applications, Applications of Deep Neural networks, Robotics and Control Applications; Deep Learning

Authors: Andreas Aakerberg ; Kamal Nasrollahi ; Christoffer B. Rasmussen and Thomas B. Moeslund

Affiliation: Aalborg University, Denmark

ISBN: 978-989-758-274-5

Keyword(s): Deep Learning, Computer Vision, Artificial Vision, RGB-D, Convolutional Neural Networks, Transfer Learning, Surface Normals.

Abstract: Object recognition is one of the important tasks in computer vision which has found enormous applications.Depth modality is proven to provide supplementary information to the common RGB modality for objectrecognition. In this paper, we propose methods to improve the recognition performance of an existing deeplearning based RGB-D object recognition model, namely the FusionNet proposed by Eitel et al. First, we showthat encoding the depth values as colorized surface normals is beneficial, when the model is initialized withweights learned from training on ImageNet data. Additionally, we show that the RGB stream of the FusionNetmodel can benefit from using deeper network architectures, namely the 16-layered VGGNet, in exchange forthe 8-layered CaffeNet. In combination, these changes improves the recognition performance with 2.2% incomparison to the original FusionNet, when evaluating on the Washington RGB-D Object Dataset.

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Paper citation in several formats:
Aakerberg A., Nasrollahi K., Rasmussen C. and Moeslund T. (2017). Depth Value Pre-Processing for Accurate Transfer Learning based RGB-D Object Recognition.In Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, ISBN 978-989-758-274-5, pages 121-128. DOI: 10.5220/0006511501210128

@conference{ijcci17,
author={Andreas Aakerberg and Kamal Nasrollahi and Christoffer B. Rasmussen and Thomas B. Moeslund},
title={Depth Value Pre-Processing for Accurate Transfer Learning based RGB-D Object Recognition},
booktitle={Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,},
year={2017},
pages={121-128},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006511501210128},
isbn={978-989-758-274-5},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,
TI - Depth Value Pre-Processing for Accurate Transfer Learning based RGB-D Object Recognition
SN - 978-989-758-274-5
AU - Aakerberg A.
AU - Nasrollahi K.
AU - Rasmussen C.
AU - Moeslund T.
PY - 2017
SP - 121
EP - 128
DO - 10.5220/0006511501210128

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