A Generic Parameter Optimization Workflow for Camera Control Algorithms

Westerhoff, Jens; Meuter, Mirko; Kummert, Anton

doi:10.1109/itsc.2015.158

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…We drove our test vehicle on roads with several tunnels in the cities of London and Toronto, Ontario, Canada, under a range of outdoor illumination conditions (bright sun, lowlevel clouds, etc.). Since changes in brightness of up to 120 dB may occur during outdoor tunnel transitions [20], tunnels are ideal environments for stress testing our predictive parameter controller.…”

Section: B Data Collection and Experiments Environmentsmentioning

confidence: 99%

Learned Camera Gain and Exposure Control for Improved Visual Feature Detection and Matching

Tomasi,

Wagstaff,

Waslander

et al. 2021

Preprint

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Successful visual navigation depends upon capturing images that contain sufficient useful information. In this paper, we explore a data-driven approach to account for environmental lighting changes, improving the quality of images for use in visual odometry (VO) or visual simultaneous localization and mapping (SLAM). We train a deep convolutional neural network model to predictively adjust camera gain and exposure time parameters such that consecutive images contain a maximal number of matchable features. The training process is fully selfsupervised: our training signal is derived from an underlying VO or SLAM pipeline and, as a result, the model is optimized to perform well with that specific pipeline. We demonstrate through extensive real-world experiments that our network can anticipate and compensate for dramatic lighting changes (e.g., transitions into and out of road tunnels), maintaining a substantially higher number of inlier feature matches than competing camera parameter control algorithms.

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Section: B Data Collection and Experiments Environmentsmentioning

confidence: 99%

Learned Camera Gain and Exposure Control for Improved Visual Feature Detection and Matching

Tomasi,

Wagstaff,

Waslander

et al. 2021

Preprint

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show abstract

“…It was proposed that camera parameters should be optimized with respect to different metrics, like image entropy in [20] and gradient information in [27]. This methodology was further summarized in [34]. In comparison with the previous work, our method emphasizes that the control of camera parameters should be optimized by the performance of specific vision applications, i.e.…”

Section: Related Workmentioning

confidence: 99%

Active Control of Camera Parameters for Object Detection Algorithms

Wu,

Tsotsos

2017

Preprint

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Camera parameters not only play an important role in determining the visual quality of perceived images, but also affect the performance of vision algorithms, for a visionguided robot. By quantitatively evaluating four object detection algorithms, with respect to varying ambient illumination, shutter speed and voltage gain, it is observed that the performance of the algorithms is highly dependent on these variables. From this observation, a novel active control of camera parameters method is proposed, to make robot vision more robust under different light conditions. Experimental results demonstrate the effectiveness of our proposed approach, which improves the performance of object detection algorithms, compared with the conventional auto-exposure algorithm.

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Automatic Brightness Control for Face Analysis in Near-Infrared Spectrum

Vugrin

Lončarić

2021

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)

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A Generic Parameter Optimization Workflow for Camera Control Algorithms

Cited by 4 publications

References 11 publications

Learned Camera Gain and Exposure Control for Improved Visual Feature Detection and Matching

Learned Camera Gain and Exposure Control for Improved Visual Feature Detection and Matching

Active Control of Camera Parameters for Object Detection Algorithms

Automatic Brightness Control for Face Analysis in Near-Infrared Spectrum

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