Gradient-Based Camera Exposure Control for Outdoor Mobile Platforms

We introduce a novel method to automatically adjust camera exposure for image processing and computer vision applications on mobile robot platforms. Because most image processing algorithms rely heavily on low-level image features that are based mainly on local gradient information, we consider that...

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Veröffentlicht in:IEEE transactions on circuits and systems for video technology Jg. 29; H. 6; S. 1569 - 1583
Hauptverfasser: Shim, Inwook, Oh, Tae-Hyun, Lee, Joon-Young, Choi, Jinwook, Choi, Dong-Geol, Kweon, In So
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.06.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Auto exposure
camera parameter control
Cameras
Computer vision
Control algorithms
Control theory
Dynamic range
Exposure
exposure control
gradient information
Image processing
Lighting
Odometers
panoramic imaging
Platforms
Robustness
stereo matching
surround-view
Vision systems
visual odometry
ISSN:1051-8215, 1558-2205
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Zusammenfassung:We introduce a novel method to automatically adjust camera exposure for image processing and computer vision applications on mobile robot platforms. Because most image processing algorithms rely heavily on low-level image features that are based mainly on local gradient information, we consider that gradient quantity can determine the proper exposure level, allowing a camera to capture the important image features in a manner robust to illumination conditions. We then extend this concept to a multi-camera system and present a new control algorithm to achieve both brightness consistency between adjacent cameras and a proper exposure level for each camera. We implement our prototype system with off-the-shelf machine-vision cameras and demonstrate the effectiveness of the proposed algorithms on practical applications, including pedestrian detection, visual odometry, surround-view imaging, panoramic imaging, and stereo matching.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2018.2846292