Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art (Foundations and Trends(r) in Computer Graphics and Vision)

Price: $99.00 – $89.52
(as of Dec 27,2024 18:26:55 UTC – Details)

Publisher ‏ : ‎ Now Publishers (July 6, 2020)
Language ‏ : ‎ English
Paperback ‏ : ‎ 326 pages
ISBN-10 ‏ : ‎ 1680836889
ISBN-13 ‏ : ‎ 978-1680836882
Item Weight ‏ : ‎ 1.09 pounds
Dimensions ‏ : ‎ 6.14 x 0.69 x 9.21 inches

Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art (Foundations and Trends(r) in Computer Graphics and Vision)

In the rapidly evolving field of autonomous vehicles, computer vision plays a crucial role in enabling these vehicles to perceive and understand their surroundings. From detecting pedestrians and other vehicles to interpreting road signs and traffic signals, computer vision algorithms are essential for ensuring the safe and reliable operation of autonomous vehicles.

However, developing effective computer vision systems for autonomous vehicles is not without its challenges. From dealing with varying lighting conditions and weather to handling complex traffic scenarios and unexpected obstacles, there are numerous problems that need to be addressed to ensure the success of autonomous driving systems.

To tackle these challenges, researchers and engineers rely on a variety of datasets to train and test their computer vision algorithms. These datasets contain labeled images and videos that help algorithms learn to recognize objects and interpret their surroundings. Some popular datasets used in the field of autonomous vehicles include KITTI, Cityscapes, and ApolloScape.

In this post, we will explore the current state of the art in computer vision for autonomous vehicles, including recent advancements in object detection, semantic segmentation, and scene understanding. We will also discuss some of the key problems that researchers are working to solve, such as improving the robustness and reliability of computer vision systems in challenging real-world scenarios.

Overall, the field of computer vision for autonomous vehicles is rapidly advancing, with researchers making significant strides in developing more accurate and reliable algorithms. By staying up to date on the latest research and trends in this area, we can help drive the future of autonomous driving forward and ultimately make our roads safer for everyone.
#Computer #Vision #Autonomous #Vehicles #Problems #Datasets #State #Art #Foundations #Trendsr #Computer #Graphics #Vision