Low Power Adaptive Time-of-Flight Imaging for Multiple Rigid Objects
Name
2019_icip_tof.pdf
Description
Accepted version
Size
1.04 MB
Format
Unknown
Checksum (MD5)
606437a0d837d380514c51f5e8abe30b
Author(s)
Noraky, James
Mathy, Charles
Cheng, Alan
Sze, Vivienne
Date Issued
September 2019
Journal
Proceedings - International Conference on Image Processing, ICIP
Publisher
IEEE
Citation
Noraky, James, Mathy, Charles, Cheng, Alan and Sze, Vivienne. 2019. "Low Power Adaptive Time-of-Flight Imaging for Multiple Rigid Objects." Proceedings - International Conference on Image Processing, ICIP, 2019-September.
Version
Author's final manuscript
Abstract
© 2019 IEEE. Time-of-flight (TOF) cameras are becoming increasingly popular for many mobile applications. To obtain accurate depth maps, TOF cameras must emit many pulses of light, which consumes a lot of power and lowers the battery life of mobile devices. However, lowering the number of emitted pulses results in noisy depth maps. To obtain accurate depth maps while reducing the overall number of emitted pulses, we propose an algorithm that adaptively varies the number of pulses to infrequently obtain high power depth maps and uses them to help estimate subsequent low power ones. To estimate these depth maps, our technique uses the previous frame by accounting for the 3D motion in the scene. We assume that the scene contains independently moving rigid objects and show that we can efficiently estimate the motions using just the data from a TOF camera. The resulting algorithm estimates 640 × 480 depth maps at 30 frames per second on an embedded processor. We evaluate our approach on data collected with a pulsed TOF camera and show that we can reduce the mean relative error of the low power depth maps by up to 64% and the number of emitted pulses by up to 81%.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Microsystems Technology Laboratories
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/icip.2019.8803579
