Multiple object tracking using an RGB-D camera by hierarchical spatiotemporal data association

Seongyong Koo, Dongheui Lee, Dong Soo Kwon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

In this paper, we propose a novel multiple object tracking method from RGB-D point set data by introducing the hierarchical spatiotemporal data association method (HSTA) in order to robustly track multiple objects without prior knowledge. HSTA is able to construct not only temporal associations between multiple objects, but also component-level spatiotemporal associations that allow the correction of falsely detected objects in the presence of various types of interaction among multiple objects. The proposed method was evaluated using the four representative interaction cases such as split, complete occlusion, partial occlusion, and multiple contacts. As a result, HSTA showed significantly more robust performance than did other temporal data association methods in the experiments.

Original languageEnglish
Title of host publicationIROS 2013
Subtitle of host publicationNew Horizon, Conference Digest - 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
Pages1113-1118
Number of pages6
DOIs
StatePublished - 2013
Externally publishedYes
Event2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013 - Tokyo, Japan
Duration: 3 Nov 20138 Nov 2013

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013
Country/TerritoryJapan
CityTokyo
Period3/11/138/11/13

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