A 3D Feature-Based Binocular Tracking Algorithm

Guang TIAN; Feihu QI; Masatoshi KIMACHI; Yue WU; Takashi IKETANI

doi:10.1093/ietisy/e89-d.7.2142

A 3D Feature-Based Binocular Tracking Algorithm

Guang TIAN, Feihu QI, Masatoshi KIMACHI, Yue WU, Takashi IKETANI

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This paper presents a 3D feature-based binocular tracking algorithm for tracking crowded people indoors. The algorithm consists of a two stage 3D feature points grouping method and a robust 3D feature-based tracking method. The two stage 3D feature points grouping method can use kernel-based ISODATA method to detect people accurately even though the part or almost full occlusion occurs among people in surveillance area. The robust 3D feature-based Tracking method combines interacting multiple model (IMM) method with a cascade multiple feature data association method. The robust 3D feature-based tracking method not only manages the generation and disappearance of a trajectory, but also can deal with the interaction of people and track people maneuvering. Experimental results demonstrate the robustness and efficiency of the proposed framework. It is real-time and not sensitive to the variable frame to frame interval time. It also can deal with the occlusion of people and do well in those cases that people rotate and wriggle.

Publication: IEICE TRANSACTIONS on Information Vol.E89-D No.7 pp.2142-2149

Publication Date: 2006/07/01

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Online ISSN: 1745-1361

DOI: 10.1093/ietisy/e89-d.7.2142

Type of Manuscript: Special Section PAPER (Special Section on Machine Vision Applications)

Category: Tracking

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Guang TIAN, Feihu QI, Masatoshi KIMACHI, Yue WU, Takashi IKETANI, "A 3D Feature-Based Binocular Tracking Algorithm" in IEICE TRANSACTIONS on Information, vol. E89-D, no. 7, pp. 2142-2149, July 2006, doi: 10.1093/ietisy/e89-d.7.2142.
Abstract: This paper presents a 3D feature-based binocular tracking algorithm for tracking crowded people indoors. The algorithm consists of a two stage 3D feature points grouping method and a robust 3D feature-based tracking method. The two stage 3D feature points grouping method can use kernel-based ISODATA method to detect people accurately even though the part or almost full occlusion occurs among people in surveillance area. The robust 3D feature-based Tracking method combines interacting multiple model (IMM) method with a cascade multiple feature data association method. The robust 3D feature-based tracking method not only manages the generation and disappearance of a trajectory, but also can deal with the interaction of people and track people maneuvering. Experimental results demonstrate the robustness and efficiency of the proposed framework. It is real-time and not sensitive to the variable frame to frame interval time. It also can deal with the occlusion of people and do well in those cases that people rotate and wriggle.
URL: https://globals.ieice.org/en_transactions/information/10.1093/ietisy/e89-d.7.2142/_p

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@ARTICLE{e89-d_7_2142,
author={Guang TIAN, Feihu QI, Masatoshi KIMACHI, Yue WU, Takashi IKETANI, },
journal={IEICE TRANSACTIONS on Information},
title={A 3D Feature-Based Binocular Tracking Algorithm},
year={2006},
volume={E89-D},
number={7},
pages={2142-2149},
abstract={This paper presents a 3D feature-based binocular tracking algorithm for tracking crowded people indoors. The algorithm consists of a two stage 3D feature points grouping method and a robust 3D feature-based tracking method. The two stage 3D feature points grouping method can use kernel-based ISODATA method to detect people accurately even though the part or almost full occlusion occurs among people in surveillance area. The robust 3D feature-based Tracking method combines interacting multiple model (IMM) method with a cascade multiple feature data association method. The robust 3D feature-based tracking method not only manages the generation and disappearance of a trajectory, but also can deal with the interaction of people and track people maneuvering. Experimental results demonstrate the robustness and efficiency of the proposed framework. It is real-time and not sensitive to the variable frame to frame interval time. It also can deal with the occlusion of people and do well in those cases that people rotate and wriggle.},
keywords={},
doi={10.1093/ietisy/e89-d.7.2142},
ISSN={1745-1361},
month={July},}

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TY - JOUR
TI - A 3D Feature-Based Binocular Tracking Algorithm
T2 - IEICE TRANSACTIONS on Information
SP - 2142
EP - 2149
AU - Guang TIAN
AU - Feihu QI
AU - Masatoshi KIMACHI
AU - Yue WU
AU - Takashi IKETANI
PY - 2006
DO - 10.1093/ietisy/e89-d.7.2142
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E89-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2006
AB - This paper presents a 3D feature-based binocular tracking algorithm for tracking crowded people indoors. The algorithm consists of a two stage 3D feature points grouping method and a robust 3D feature-based tracking method. The two stage 3D feature points grouping method can use kernel-based ISODATA method to detect people accurately even though the part or almost full occlusion occurs among people in surveillance area. The robust 3D feature-based Tracking method combines interacting multiple model (IMM) method with a cascade multiple feature data association method. The robust 3D feature-based tracking method not only manages the generation and disappearance of a trajectory, but also can deal with the interaction of people and track people maneuvering. Experimental results demonstrate the robustness and efficiency of the proposed framework. It is real-time and not sensitive to the variable frame to frame interval time. It also can deal with the occlusion of people and do well in those cases that people rotate and wriggle.
ER -