Camera Exposure Control for Robust Robot Vision with Noise-Aware Image Quality Assessment
Ukcheol Shin (KAIST), Jinsun Park (KAIST), Gyumin Shim (KAIST), Francois Rameau (KAIST), In So Kweon (KAIST)
Abstract
In this paper, we propose a noise-aware exposure control algorithm for robust robot vision. Our method aims to capture best-exposed images, which can boost the performance of various computer vision and robotics tasks. For this purpose, we carefully design an image quality metric that captures complementary quality attributes and ensures light-weight computation. Specifically, our metric consists of a combination of image gradient, entropy, and noise metrics. The synergy of these measures allows the preservation of sharp edges and rich texture in the image while maintaining a low noise level. Using this novel metric, we propose a real-time and fully automatic exposure and gain control technique based on the Nelder-Mead method. To illustrate the effectiveness of our technique, a large set of experimental results demonstrates the higher qualitative and quantitative performance compared with conventional approaches.
Explanation Video Material
Exposure Control Dataset & Source Code
In this paper, we provide a unique dataset developed specifically to compare exposure control algorithms. The composition of this dataset is as follows.
• HW setup : a stereo camera system with 20 cm baseline acquiring synchronized 1600 x 1200 px images.
• # of scene : Total 25 scene (10 indoor, 15 outdoor)
• # of image : Each scene consist of 550 x 2 images
– Outdoor Environment
Exposure time : [0.1 - 7.45 ms] with 0.15 ms interval
Gain : [0 - 20]dB with 2dB interval
– Indoor Environment
Exposure time : [4 - 67 ms] with 3 ms interval
Gain : [0 - 24]dB with 1dB interval
• # of object class : 13 Object Class
– Person, Bicycle, Car, Firehydrant, Backpack, Sports ball, Chair, Mouse, Keyboard, Cellphone, Book, Scissors, and TV.
– However, some objects appear very rarely, we need to acquire more dataset.
• Download link : https://drive.google.com/file/d/1DUeByL_ADzGyRpqc0iXA-DxZwLOd5Q-U/view?usp=sharing
• Github link : https://github.com/WookCheolShin/Noise-AwareCameraExposureControl
Publication
"Camera Exposure Control for Robust Robot Vision with Noise-Aware Image Quality Assessment" [PDF]
Ukcheol Shin, Jinsun Park, Gyumin Shim, Francois Rameau, and In So Kweon.
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019
Acknowledgments
This work was supported in part by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT).