Stereo vision during adverse weather — Using priors to increase robustness in real-time stereo vision
| Autor: | Nicolai Schneider, Uwe Franke, Reto Stalder, Stefan Gehrig |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Stereo cameras
business.industry Computer science 3D reconstruction Robotics 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Object detection 010309 optics Stereopsis Robustness (computer science) 0103 physical sciences Signal Processing Prior probability 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Computer Vision and Pattern Recognition Artificial intelligence business computer Computer stereo vision |
| Zdroj: | Image and Vision Computing. 68:28-39 |
| ISSN: | 0262-8856 |
| Popis: | Stereo vision can deliver a dense 3D reconstruction of the environment in real-time for driver assistance as well as autonomous driving. Semi-Global Matching (SGM) is a popular method of choice for solving this task which is already in use for production vehicles. Despite the enormous progress in the field and the high level of performance of modern stereo methods, one key challenge remains: robust stereo vision in automotive scenarios during rain, snow and darkness. Under these circumstances, current methods generate strong temporal noise, many disparity outliers and false positives on object level. These problems are addressed in this work by regularizing stereo vision via prior information. We formulate a temporal prior and a scene prior, which we apply to SGM in order to overcome the deficiencies. The temporal prior integrates knowledge from the previous disparity map to exploit the high temporal correlation, the scene prior exploits knowledge of a representative traffic scene. Using these priors, the object detection rate improves significantly on a driver assistance dataset of 3000 frames including bad weather while reducing the rate of erroneous object detections. We also outperform the ECCV Robust Vision Challenge 2012 winner, iSGM, on this dataset. In addition, results are presented for the KITTI dataset, even showing improvements under good weather conditions when exploiting the temporal prior. We also show that the temporal and scene priors are easy and efficient to implement on a hybrid CPU/reconfigurable hardware platform. The use of these priors can be extended to other application areas such as mobile robotics. |
| Databáze: | OpenAIRE |
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