3D noise-resistant segmentation and tracking of unknown and occluded objects using integral imaging

Doron Aloni; Jae Hyun Jung; Yitzhak Yitzhaky

doi:10.1117/12.2278244

3D noise-resistant segmentation and tracking of unknown and occluded objects using integral imaging

Doron Aloni, Jae Hyun Jung, Yitzhak Yitzhaky

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Three dimensional (3D) object segmentation and tracking can be useful in various computer vision applications, such as: object surveillance for security uses, robot navigation, etc. We present a method for 3D multiple-object tracking using computational integral imaging, based on accurate 3D object segmentation. The method does not employ object detection by motion analysis in a video as conventionally performed (such as background subtraction or block matching). This means that the movement properties do not significantly affect the detection quality. The object detection is performed by analyzing static 3D image data obtained through computational integral imaging With regard to previous works that used integral imaging data in such a scenario, the proposed method performs the 3D tracking of objects without prior information about the objects in the scene, and it is found efficient under severe noise conditions.

Original language	American English
Title of host publication	Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies
Editors	Yitzhak Yitzhaky, Robert James Stokes, Henri Bouma, Felicity Carlysle-Davies
Publisher	SPIE
ISBN (Electronic)	9781510613461
DOIs	https://doi.org/10.1117/12.2278244
State	Published - 1 Jan 2017
Event	Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies 2017 - Warsaw, Poland Duration: 11 Sep 2017 → 12 Sep 2017

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	10441

Conference

Conference	Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies 2017
Country/Territory	Poland
City	Warsaw
Period	11/09/17 → 12/09/17

Keywords

Computational integral imaging
Three dimensional object tracking
Three dimensional segmentation

All Science Journal Classification (ASJC) codes

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

Access to Document

10.1117/12.2278244

Cite this

Aloni, D., Jung, J. H., & Yitzhaky, Y. (2017). 3D noise-resistant segmentation and tracking of unknown and occluded objects using integral imaging. In Y. Yitzhaky, R. J. Stokes, H. Bouma, & F. Carlysle-Davies (Eds.), Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies Article 104410D (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10441). SPIE. https://doi.org/10.1117/12.2278244

Aloni, Doron ; Jung, Jae Hyun ; Yitzhaky, Yitzhak. / 3D noise-resistant segmentation and tracking of unknown and occluded objects using integral imaging. Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies. editor / Yitzhak Yitzhaky ; Robert James Stokes ; Henri Bouma ; Felicity Carlysle-Davies. SPIE, 2017. (Proceedings of SPIE - The International Society for Optical Engineering).

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abstract = "Three dimensional (3D) object segmentation and tracking can be useful in various computer vision applications, such as: object surveillance for security uses, robot navigation, etc. We present a method for 3D multiple-object tracking using computational integral imaging, based on accurate 3D object segmentation. The method does not employ object detection by motion analysis in a video as conventionally performed (such as background subtraction or block matching). This means that the movement properties do not significantly affect the detection quality. The object detection is performed by analyzing static 3D image data obtained through computational integral imaging With regard to previous works that used integral imaging data in such a scenario, the proposed method performs the 3D tracking of objects without prior information about the objects in the scene, and it is found efficient under severe noise conditions.",

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Aloni, D, Jung, JH & Yitzhaky, Y 2017, 3D noise-resistant segmentation and tracking of unknown and occluded objects using integral imaging. in Y Yitzhaky, RJ Stokes, H Bouma & F Carlysle-Davies (eds), Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies., 104410D, Proceedings of SPIE - The International Society for Optical Engineering, vol. 10441, SPIE, Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies 2017, Warsaw, Poland, 11/09/17. https://doi.org/10.1117/12.2278244

3D noise-resistant segmentation and tracking of unknown and occluded objects using integral imaging. / Aloni, Doron; Jung, Jae Hyun; Yitzhaky, Yitzhak.
Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies. ed. / Yitzhak Yitzhaky; Robert James Stokes; Henri Bouma; Felicity Carlysle-Davies. SPIE, 2017. 104410D (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10441).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Jung, Jae Hyun

AU - Yitzhaky, Yitzhak

N1 - Publisher Copyright: © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - Three dimensional (3D) object segmentation and tracking can be useful in various computer vision applications, such as: object surveillance for security uses, robot navigation, etc. We present a method for 3D multiple-object tracking using computational integral imaging, based on accurate 3D object segmentation. The method does not employ object detection by motion analysis in a video as conventionally performed (such as background subtraction or block matching). This means that the movement properties do not significantly affect the detection quality. The object detection is performed by analyzing static 3D image data obtained through computational integral imaging With regard to previous works that used integral imaging data in such a scenario, the proposed method performs the 3D tracking of objects without prior information about the objects in the scene, and it is found efficient under severe noise conditions.

AB - Three dimensional (3D) object segmentation and tracking can be useful in various computer vision applications, such as: object surveillance for security uses, robot navigation, etc. We present a method for 3D multiple-object tracking using computational integral imaging, based on accurate 3D object segmentation. The method does not employ object detection by motion analysis in a video as conventionally performed (such as background subtraction or block matching). This means that the movement properties do not significantly affect the detection quality. The object detection is performed by analyzing static 3D image data obtained through computational integral imaging With regard to previous works that used integral imaging data in such a scenario, the proposed method performs the 3D tracking of objects without prior information about the objects in the scene, and it is found efficient under severe noise conditions.

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Aloni D, Jung JH, Yitzhaky Y. 3D noise-resistant segmentation and tracking of unknown and occluded objects using integral imaging. In Yitzhaky Y, Stokes RJ, Bouma H, Carlysle-Davies F, editors, Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies. SPIE. 2017. 104410D. (Proceedings of SPIE - The International Society for Optical Engineering). doi: 10.1117/12.2278244

3D noise-resistant segmentation and tracking of unknown and occluded objects using integral imaging

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Keywords

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