3D Object Tracking with Neuromorphic Event Cameras via Image Reconstruction

Hadar Cohen Duwek; Avinoam Bitton; Elishai Ezra Tsur

doi:10.1109/BioCAS49922.2021.9644652

3D Object Tracking with Neuromorphic Event Cameras via Image Reconstruction

Hadar Cohen Duwek, Avinoam Bitton, Elishai Ezra Tsur

מדעי המחשב

פרסום מחקרי: פרק בספר / בדוח / בכנס › פרסום בספר כנס › ביקורת עמיתים

תקציר

One of the first and most remarkable successes in neuromorphic (brain-inspired) engineering was the development of bio-inspired event cameras, which communicate transients in luminance as events. Here we evaluate the combination of the Channel and Spatial Reliability Tracking (CSRT) algorithm and the LapDepth neural network for the implementation of 3D object tracking with event cameras. We show that following image reconstruction, implemented using the FireNet convolution neural network, visual features are augmented, dramatically increasing tracking performance. We utilized the 3D tracker to neuromorphically represent error-correcting signals. These error-correcting signals can further be used for motion correction in adaptive neurorobotics.

שפה מקורית	אנגלית
כותר פרסום המארח	BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings
מוציא לאור	Institute of Electrical and Electronics Engineers Inc.
מסת"ב (אלקטרוני)	9781728172040
מזהי עצם דיגיטלי (DOIs)	https://doi.org/10.1109/BioCAS49922.2021.9644652
סטטוס פרסום	פורסם - 2021
אירוע	2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021 - Virtual, Online, גרמניה משך הזמן: 6 אוק׳ 2021 → 9 אוק׳ 2021

סדרות פרסומים

שם	BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings

כנס

כנס	2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021
מדינה/אזור	גרמניה
עיר	Virtual, Online
תקופה	6/10/21 → 9/10/21

הערה ביבליוגרפית

Publisher Copyright:
© 2021 IEEE.

גישה למסמך

10.1109/BioCAS49922.2021.9644652

קבצים וקישורים אחרים

Link to publication in Scopus

פורמט ציטוט ביבליוגרפי

Duwek, H. C., Bitton, A., & Tsur, E. E. (2021). 3D Object Tracking with Neuromorphic Event Cameras via Image Reconstruction. ב- BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings (BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BioCAS49922.2021.9644652

@inproceedings{c8595a2adf5d40eda69374ba1e60aea6,

title = "3D Object Tracking with Neuromorphic Event Cameras via Image Reconstruction",

abstract = "One of the first and most remarkable successes in neuromorphic (brain-inspired) engineering was the development of bio-inspired event cameras, which communicate transients in luminance as events. Here we evaluate the combination of the Channel and Spatial Reliability Tracking (CSRT) algorithm and the LapDepth neural network for the implementation of 3D object tracking with event cameras. We show that following image reconstruction, implemented using the FireNet convolution neural network, visual features are augmented, dramatically increasing tracking performance. We utilized the 3D tracker to neuromorphically represent error-correcting signals. These error-correcting signals can further be used for motion correction in adaptive neurorobotics.",

keywords = "CSRT, FireNet, LapDepth, dynamic visual sensors, neural engineering framework, neuromorphic computing",

author = "Duwek, {Hadar Cohen} and Avinoam Bitton and Tsur, {Elishai Ezra}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021 ; Conference date: 06-10-2021 Through 09-10-2021",

year = "2021",

doi = "10.1109/BioCAS49922.2021.9644652",

language = "אנגלית",

series = "BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings",

address = "ארצות הברית",

}

Duwek, HC, Bitton, A & Tsur, EE 2021, 3D Object Tracking with Neuromorphic Event Cameras via Image Reconstruction. ב- BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings. BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings, Institute of Electrical and Electronics Engineers Inc., 2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021, Virtual, Online, גרמניה, 6/10/21. https://doi.org/10.1109/BioCAS49922.2021.9644652

3D Object Tracking with Neuromorphic Event Cameras via Image Reconstruction. / Duwek, Hadar Cohen; Bitton, Avinoam; Tsur, Elishai Ezra.
BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. (BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings).

פרסום מחקרי: פרק בספר / בדוח / בכנס › פרסום בספר כנס › ביקורת עמיתים

TY - GEN

T1 - 3D Object Tracking with Neuromorphic Event Cameras via Image Reconstruction

AU - Duwek, Hadar Cohen

AU - Bitton, Avinoam

AU - Tsur, Elishai Ezra

PY - 2021

Y1 - 2021

N2 - One of the first and most remarkable successes in neuromorphic (brain-inspired) engineering was the development of bio-inspired event cameras, which communicate transients in luminance as events. Here we evaluate the combination of the Channel and Spatial Reliability Tracking (CSRT) algorithm and the LapDepth neural network for the implementation of 3D object tracking with event cameras. We show that following image reconstruction, implemented using the FireNet convolution neural network, visual features are augmented, dramatically increasing tracking performance. We utilized the 3D tracker to neuromorphically represent error-correcting signals. These error-correcting signals can further be used for motion correction in adaptive neurorobotics.

AB - One of the first and most remarkable successes in neuromorphic (brain-inspired) engineering was the development of bio-inspired event cameras, which communicate transients in luminance as events. Here we evaluate the combination of the Channel and Spatial Reliability Tracking (CSRT) algorithm and the LapDepth neural network for the implementation of 3D object tracking with event cameras. We show that following image reconstruction, implemented using the FireNet convolution neural network, visual features are augmented, dramatically increasing tracking performance. We utilized the 3D tracker to neuromorphically represent error-correcting signals. These error-correcting signals can further be used for motion correction in adaptive neurorobotics.

KW - CSRT

KW - FireNet

KW - LapDepth

KW - dynamic visual sensors

KW - neural engineering framework

KW - neuromorphic computing

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PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021

Y2 - 6 October 2021 through 9 October 2021

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3D Object Tracking with Neuromorphic Event Cameras via Image Reconstruction

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