Road Detection Based on Illuminant Invariance

By José Manuel Álvarez, Antonio M. López. IEEE transactions on intelligent transportation systems (Print), 2011

333 citations recorded by the source corpus.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/37e99517a808c8768e590c77a9e06744e0f1d3b7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Fatma Güney
2. Joel Janai
3. Yingying Zhu
4. Luc Van Gool
5. Shih-Lung Shaw
6. Dengxin Dai
7. Long Chen
8. Mingkun Yang

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Dengxin Dai
3. Andreas Geiger
4. Ming-Hsuan Yang
5. Fatma Güney
6. Christos Sakaridis
7. Joel Janai
8. Aseem Behl

In Ocean Engineering

1. Luc Van Gool
2. Dengxin Dai
3. Fatma Güney
4. Andreas Geiger
5. Christos Sakaridis

Citations in the corpus

• Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime (2018, background): “illumination changes for robust road area detection [2] and semantic labeling [23].”
• Cascaded Segmentation-Detection Networks for Text-Based Traffic Sign Detection (2018, background): “S INCE texts in traffic signs have precise and rich semantic information related to traffic condition, text-based traffic sign detection plays an important role in the autonomous driving and auxiliary driving of Intelligent Transportation System, just as traffic congestion detection [1], road detect”
• A Sensor-Fusion Drivable-Region and Lane-Detection System for Autonomous Vehicle Navigation in Challenging Road Scenarios (2014, methodology): “Álvarez and Lopez [28] used a shadow-invariant feature space in combination with a model-based classifier.”
• Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, background): “In a follow-up work, Álvarez & López (2011) convert the image into an illuminant invariant feature space to make their method robust to shadows and then apply a classifier to assign a semantic label to each pixel.”
• Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation (2019, methodology): “Contrary to these domain-specific methods, previous work also includes both methods designed for robustness to illumination changes, by employing domaininvariant representations [1, 25] or fusing information from complementary modalities and spectra [33], and datasets with adverse illumination [21, ”
• A Hybrid Vision-Map Method for Urban Road Detection (2017, background): “Some approaches attempt to attenuate the shadow influence with an illuminant invariant space to detect the road [38, 39].”