Dataset for Robust and Accurate Leading Vehicle Velocity Recognition

Autor:	Ogawa, Genya, Saito, Toru, Aoi, Noriyuki
Rok vydání:	2022
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Recognition of the surrounding environment using a camera is an important technology in Advanced Driver-Assistance Systems and Autonomous Driving, and recognition technology is often solved by machine learning approaches such as deep learning in recent years. Machine learning requires datasets for learning and evaluation. To develop robust recognition technology in the real world, in addition to normal driving environment, data in environments that are difficult for cameras such as rainy weather or nighttime are essential. We have constructed a dataset that one can benchmark the technology, targeting the velocity recognition of the leading vehicle. This task is an important one for the Advanced Driver-Assistance Systems and Autonomous Driving. The dataset is available at https://signate.jp/competitions/657 Comment: 5 pages, 9 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2204.12717 Zobrazit plný text záznamu View this record from Arxiv