Deeply Exploit Depth Information for Object Detection

Saihui Hou, Zilei Wang, Feng Wu; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016, pp. 19-27

Abstract


This paper addresses the issue on how to more effectively coordinate the depth with RGB aiming at boosting the performance of RGB-D object detection. Particularly, we investigate two primary ideas under the CNN model: property derivation and property fusion. Firstly, we propose that the depth can be utilized not only as a type of extra information besides RGB but also to derive more visual properties for comprehensively describing the objects of interest. So a two-stage learning framework consisting of property derivation and fusion is constructed. Secondly, we explore the fusion method of different properties in feature learning, which is boiled down to, under the CNN model, from which layer the properties should be fused together. The analysis shows that different semantic properties should be learned separately and combined before passing into the final classifier. We evaluate the proposed method on the challenging dataset, and have achieved state-of-the-art performance.

Related Material


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[bibtex]
@InProceedings{Hou_2016_CVPR_Workshops,
author = {Hou, Saihui and Wang, Zilei and Wu, Feng},
title = {Deeply Exploit Depth Information for Object Detection},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2016}
}