التفاصيل البيبلوغرافية
| العنوان: |
TrafficCAM: A Versatile Dataset for Traffic Flow Segmentation |
| المؤلفون: |
Deng, Zhongying, Chen, Yanqi, Liu, Lihao, Wang, Shujun, Ke, Rihuan, Schonlieb, Carola-Bibiane, Aviles-Rivero, Angelica I |
| سنة النشر: |
2022 |
| المجموعة: |
Computer Science |
| مصطلحات موضوعية: |
Computer Science - Computer Vision and Pattern Recognition |
| الوصف: |
Traffic flow analysis is revolutionising traffic management. Qualifying traffic flow data, traffic control bureaus could provide drivers with real-time alerts, advising the fastest routes and therefore optimising transportation logistics and reducing congestion. The existing traffic flow datasets have two major limitations. They feature a limited number of classes, usually limited to one type of vehicle, and the scarcity of unlabelled data. In this paper, we introduce a new benchmark traffic flow image dataset called TrafficCAM. Our dataset distinguishes itself by two major highlights. Firstly, TrafficCAM provides both pixel-level and instance-level semantic labelling along with a large range of types of vehicles and pedestrians. It is composed of a large and diverse set of video sequences recorded in streets from eight Indian cities with stationary cameras. Secondly, TrafficCAM aims to establish a new benchmark for developing fully-supervised tasks, and importantly, semi-supervised learning techniques. It is the first dataset that provides a vast amount of unlabelled data, helping to better capture traffic flow qualification under a low cost annotation requirement. More precisely, our dataset has 4,402 image frames with semantic and instance annotations along with 59,944 unlabelled image frames. We validate our new dataset through a large and comprehensive range of experiments on several state-of-the-art approaches under four different settings: fully-supervised semantic and instance segmentation, and semi-supervised semantic and instance segmentation tasks. Our benchmark dataset will be released. |
| نوع الوثيقة: |
Working Paper |
| URL الوصول: |
http://arxiv.org/abs/2211.09620 |
| رقم الانضمام: |
edsarx.2211.09620 |
| قاعدة البيانات: |
arXiv |