通过多摄像头域自适应进行无监督车辆计数（CS CV）---刘持诚

监测城市中的车辆流量是改善城市环境和市民生活质量的关键问题。图像是感知和评估大面积车辆流动的最佳传感方式。目前图像中的车辆计数技术依赖于大量的注释数据，随着新的摄像头被添加到系统中，阻碍了其对城市规模的可扩展性。这是处理物理系统时经常出现的问题，也是机器学习和人工智能的一个关键研究领域。我们提出并讨论了一种新的方法，通过多摄像头域自适应，使用很少的标注数据来设计基于图像的车辆密度估计器。

原文题目：Unsupervised Vehicle Counting via Multiple Camera Domain Adaptation

原文：Monitoring vehicle flow in cities is a crucial issue to improve the urban environment and quality of life of citizens. Images are the best sensing modality to perceive and asses the flow of vehicles in large areas. Current technologies for vehicle counting in images hinge on large quantities of annotated data, preventing their scalability to city-scale as new cameras are added to the system. This is a recurrent problem when dealing with physical systems and a key research area in Machine Learning and AI. We propose and discuss a new methodology to design image-based vehicle density estimators with few labeled data via multiple camera domain adaptation.

原文作者：Luca Ciampi, Carlos Santiago, Joao Paulo Costeira, Claudio Gennaro, Giuseppe Amato

原文地址：https://arxiv.org/abs/2004.09251