This chapter examines different vision‐based commercial solutions for real‐live problems related to vehicles. It is worth mentioning the recent astonishing performance of deep convolutional neural networks (DCNNs) in difficult visual tasks such as image classification, object recognition/localization/detection, and semantic segmentation. In fact, different DCNN architectures are already being explored for low‐level tasks such as optical flow and disparity computation, and higher level ones such as place recognition. Accordingly, multimodal perception is and is going to be a very relevant topic too. An interesting type of sensor announced by the end of 2016 is the solid‐state lidar, which promises a low‐cost array of depth information that can complement the visual spectrum. In summary, the academic and industry communities working on computer vision for vehicles are facing a really exciting revolution which, undoubtedly, will bring enormous social benefits and unbelievable technological advances.
Closing Notes
Computer Vision in Vehicle Technology ; 161-163
2017-02-28
3 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch