Closed-Loop Region of Interest Enabling High Spatial and Temporal Resolutions in Object Detection and Tracking via Wireless Camera
Name
Closed-Loop_Region_of_Interest_Enabling_High_Spatial_and_Temporal_Resolutions_in_Object_Detection_and_Tracking_via_Wireless_Camera.pdf
Description
Published version
Size
2.03 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Chen, Jack
Huang, Hen-Wei
Rupp, Philipp
Sinha, Anjali
Ehmke, Claas
Traverso, Giovanni
Date Issued
2021
Journal
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Chen, Jack, Huang, Hen-Wei, Rupp, Philipp, Sinha, Anjali, Ehmke, Claas et al. 2021. "Closed-Loop Region of Interest Enabling High Spatial and Temporal Resolutions in Object Detection and Tracking via Wireless Camera." IEEE Access, 9.
Version
Final published version
Abstract
The trade-off between spatial and temporal resolution remains a fundamental challenge in
machine vision. A captured image often contains a significant amount of redundant information, and only
a small region of interest (ROI) is necessary for object detection and tracking. In this paper, we first
systematically characterize the effects of ROI on camera capturing, data transmission, and image processing.
We then present the closed-loop ROI algorithm capable of high spatial and temporal resolution as well as
wide scanning field of view (FOV) in single and multi-object detection and tracking via real-time wireless
video streaming. With the feedback from real-time object tracking, the wireless camera is able to capture
and transmit only the ROI which in turn enhances both the spatial and temporal resolution in object tracking.
In addition, the proposed approach can still maintain a large FOV by processing regions outside of the
ROI at lower spatial and temporal resolutions. When applied to a high spatial resolution wireless stream
(5 MegaPixels), the closed-loop ROI algorithm improves the temporal resolution by up to 10× (from 2.4 FPS
to 22.5 FPS). Specifically, camera processing is improved by up to 4.7×, data transmission is improved by
up to 160×, and PC processing is improved by up to 2.5×. In a person tracking experiment, the closed-loop
ROI algorithm enables a wide-angle camera to outperform both a normal wide-angle camera–which suffers
from poor temporal resolution and motion blur–and a pan & tilt camera–which cannot automatically refresh
tracking after the tracking is lost.
MIT Department
Koch Institute for Integrative Cancer Research at MIT
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ACCESS.2021.3086499
