Feature++: Cross dimension feature fusion for road detection

He, Wenli; Cai, Guorong; Zhong, Zhun; Su, Songzhi

doi:10.1109/icassp.2017.7952439

Cited by 2 publications

(1 citation statement)

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“…The best fusion strategy is selected by evaluating the feature fusions on the KITTI ROAD benchmark. Part of the experimental results is published [8]. Compared to our previous work, (i) more experiments are added, especially the performance of deep learning‐based methods; (ii) implementation details are well‐described and the corresponding source code will be released; and (iii) related works on the state of the arts of road detection is included.…”

Section: Introductionmentioning

confidence: 99%

Combining 2D and 3D features to improve road detection based on stereo cameras

Cai

et al. 2018

IET Computer Vision

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Road detection is a fundamental component of autonomous driving systems since it provides valid space and candidate regions of objects for driving decision. The core of road detection methods is extracting effective and discriminative features. Since two-dimensional (2D) and 3D features are complementary, the authors propose a robust multi-feature combination and optimisation framework for stereo image pairs, called Feature++. First, several 2D and 3D features such as Gabor and plane are, respectively, extracted after the generation of 2D super-pixel and a 3D depth image from stereo matching. Second, the combined features are fed into a three-layer shallow neural network classifier to decide whether a super-pixel is road region or not. Finally, the classified results are further refined using fully connected conditional random field (CRF), taking the content information into consideration. We extensively evaluate the performance of four 2D features, four 3D features, and their combinations. Experiments conducted on the KITTI ROAD benchmark show that (i) the combinations of 2D and 3D features greatly improve the road detection performance and (ii) using CRF as a refinement step is necessary. Overall, their proposed 'Feature + +' method outperforms most manually designed features, and is comparable with state-of-the-art methods that are based on deep learning methods.

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Section: Introductionmentioning

confidence: 99%