CAR -- Cityscapes Attributes Recognition A Multi-category Attributes Dataset for Autonomous Vehicles
Kareem Metwaly, Aerin Kim, Elliot Branson, Vishal Monga

TL;DR
This paper introduces the CAR dataset, an extension of Cityscapes with rich attribute annotations for objects, aimed at improving scene understanding in autonomous vehicle perception systems.
Contribution
The paper presents a new, richly annotated dataset for object attributes in autonomous driving scenes, enhancing existing datasets for better scene comprehension.
Findings
Over 32,000 instances annotated with attributes
Structured taxonomy tailored for autonomous driving
API developed for easy dataset access
Abstract
Self-driving vehicles are the future of transportation. With current advancements in this field, the world is getting closer to safe roads with almost zero probability of having accidents and eliminating human errors. However, there is still plenty of research and development necessary to reach a level of robustness. One important aspect is to understand a scene fully including all details. As some characteristics (attributes) of objects in a scene (drivers' behavior for instance) could be imperative for correct decision making. However, current algorithms suffer from low-quality datasets with such rich attributes. Therefore, in this paper, we present a new dataset for attributes recognition -- Cityscapes Attributes Recognition (CAR). The new dataset extends the well-known dataset Cityscapes by adding an additional yet important annotation layer of attributes of objects in each image.…
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Taxonomy
TopicsAdvanced Neural Network Applications · Video Surveillance and Tracking Methods · Autonomous Vehicle Technology and Safety
