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3-D Laser-Based Multiclass and Multiview Object Detection in Cluttered Indoor Scenes

Zhang, Xuesong and Zhuang, Yan and Hu, Huosheng and Wang, Wei (2017) '3-D Laser-Based Multiclass and Multiview Object Detection in Cluttered Indoor Scenes.' IEEE Transactions on Neural Networks and Learning Systems, 28 (1). 177 - 190. ISSN 2162-2388

IEEE-TNNLS-V27-2016-P-4414.pdf - Accepted Version

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This paper investigates the problem of multiclass and multiview 3-D object detection for service robots operating in a cluttered indoor environment. A novel 3-D object detection system using laser point clouds is proposed to deal with cluttered indoor scenes with a fewer and imbalanced training data. Raw 3-D point clouds are first transformed to 2-D bearing angle images to reduce the computational cost, and then jointly trained multiple object detectors are deployed to perform the multiclass and multiview 3-D object detection. The reclassification technique is utilized on each detected low confidence bounding box in the system to reduce false alarms in the detection. The RUS-SMOTEboost algorithm is used to train a group of independent binary classifiers with imbalanced training data. Dense histograms of oriented gradients and local binary pattern features are combined as a feature set for the reclassification task. Based on the dalian university of technology (DUT)-3-D data set taken from various office and household environments, experimental results show the validity and good performance of the proposed method.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 21 Oct 2016 13:17
Last Modified: 29 Jun 2018 11:15

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