智能系统与机器人
Paper
3-D Laser-Based Multiclass and Multiview Object Detection in Cluttered Indoor Scenes
Release Time:2019-03-12
Hits:
Indexed by:
Journal Article
First Author:
Zhang, Xuesong
Correspondence Author:
Zhang, XS (reprint author), Dalian Univ Technol, Sch Control Sci & Engn, Dalian 116024, Peoples R China.
Co-author:
Wang, Wei,Zhuang, Yan,Hu, Huosheng
Date of Publication:
2017-01-01
Journal:
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Included Journals:
Scopus、EI、SCIE
Document Type:
J
Volume:
28
Issue:
1
Page Number:
177-190
ISSN:
2162-237X
Key Words:
Imbalanced learning; laser scanning; multiclass and multiview 3-D object
detection; multitask learning; sharing features
Abstract:
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.