# Gradient-based Camera Exposure Control for Outdoor Mobile Platforms

arXiv: https://arxiv.org/abs/1708.07338 · HTML: https://mlsanity.com/paper/1708.07338/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2017.

## Built on by (most cited first, 9 of 9)

- [Multi-sensor large-scale dataset for multi-view 3D reconstruction](https://mlsanity.com/paper/2203.06111/) (2022)
- [Learning to Control Camera Exposure via Reinforcement Learning](https://mlsanity.com/paper/2404.01636/) (2024)
- [Monitoring and Adapting the Physical State of a Camera for Autonomous Vehicles](https://mlsanity.com/paper/2112.05456/) (2021)
- [Dual Exposure Stereo for Extended Dynamic Range 3D Imaging](https://mlsanity.com/paper/2412.02351/) (2024)
- [Reproducible Evaluation of Camera Auto-Exposure Methods in the Field: Platform, Benchmark and Lessons Learned](https://mlsanity.com/paper/2506.18844/) (2025)
- [Improving the perception of visual fiducial markers in the field using Adaptive Active Exposure Control](https://mlsanity.com/paper/2404.12055/) (2024)
- [Dynamic Exposure Burst Image Restoration](https://mlsanity.com/paper/2603.21784/) (2026)
- [Lucky High Dynamic Range Smartphone Imaging](https://mlsanity.com/paper/2604.19976/) (2026)
- [Pixel-wise Exposure for Highly Robust In-Vehicle Remote-PPG](https://mlsanity.com/paper/2609.36607/) (2026)

## Abstract (arXiv)

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.