TY - GEN
T1 - 3D noise-resistant segmentation and tracking of unknown and occluded objects using integral imaging
AU - Aloni, Doron
AU - Jung, Jae Hyun
AU - Yitzhaky, Yitzhak
N1 - Funding Information: This work was supported in part by the Office of the Assistant Secretary of Defense for Health Affairs, through the Clinical and Rehabilitative Medicine Research Program. Joint Program Committee 8, Neurosensory and Rehabilitative Research Program under Award No. W81XWH-16-1-0033. Opinions, interpretations, conclusions, and recommendations are those of the author and are not necessarily endorsed by the Department of Defense. 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.
KW - Computational integral imaging
KW - Three dimensional object tracking
KW - Three dimensional segmentation
UR - http://www.scopus.com/inward/record.url?scp=85038418020&partnerID=8YFLogxK
U2 - https://doi.org/10.1117/12.2278244
DO - https://doi.org/10.1117/12.2278244
M3 - Conference contribution
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies
A2 - Yitzhaky, Yitzhak
A2 - Stokes, Robert James
A2 - Bouma, Henri
A2 - Carlysle-Davies, Felicity
PB - SPIE
T2 - Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies 2017
Y2 - 11 September 2017 through 12 September 2017
ER -