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

Paper Details

Date Published: 5 October 2017
PDF: 7 pages
Proc. SPIE 10441, Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies, 104410D (5 October 2017); doi: 10.1117/12.2278244
Show Author Affiliations
Doron Aloni, Ben-Gurion Univ. of the Negev (Israel)
Jae-Hyun Jung, The Schepens Eye Research Institute, Harvard Medical School (United States)
Yitzhak Yitzhaky, Ben-Gurion Univ. of the Negev (Israel)


Published in SPIE Proceedings Vol. 10441:
Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies
Henri Bouma; Felicity Carlysle-Davies; Robert James Stokes; Yitzhak Yitzhaky, Editor(s)

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