Computer Vision in Vehicle Technology

Chapter 8Closing Notes

Antonio M. López

ADAS Group, Computer Vision Center (CVC) and Computer Science Department, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain

With more than 20 years of experience in computer vision so far and after going through the different chapters of this book, one feels astonished about the progress made in this field. The contribution of computer vision for vehicular technologies is beyond doubt, in land, sea, and air. The current level of maturity has been possible thanks to advances in different axes, namely, in a continuous improvement of cameras (cost, resolution, frame rate, size, weight), more powerful processing units ready for on-board embedding (CPUs, GPUs, FPGAs), more publicly available data sets and evaluation protocols (e.g., the KITTI Vision Benchmark Suite), and of course more and more consolidated computer vision and machine learning algorithms for discriminative feature extraction, matching, stereo and optical flow computation, object detection and tracking, localization and mapping, spatiotemporal reasoning, semantic segmentation, and so on.

Overall, we can find different vision-based commercial solutions for real-live problems related to vehicles (especially for driver assistance). However, despite this great progress, if we compare the state of the art of computer vision for vehicles with the visual capabilities of humans, one can safely say that still there is a large room for improvement. Just taking the driver task ...