摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑-每日新闻-调查人员发布了关于人工智能的新报告。根据Newsrx编辑在德国莱比锡的新闻报道，研究表明，“汽车数量的增加和街道速度的限制给城市带来了重大挑战，不仅适用于移动交通，也适用于静止交通。对停车位的追求加剧了交通拥堵、噪音和空气污染，特别是在居民区。”我们的新闻记者从勒伊比锡大学的研究中获得了一句话：“为了针对这些挑战制定有效的停车解决方案，一个关于可用停车位容量、其使用情况和停车类型的可信数据基础至关重要。收集这些数据目前非常耗时，需要人工标记和街道检查。此外，停车位管理的研究主要集中在用固定摄像机监控指定的停车场，以识别空闲或占用的停车位。本文介绍了一种基于计算机视觉的自动采集停车空间和停车类型信息的方法,该方法将路景和航拍图像结合起来,通过运动摄像机记录下来,解决了地理参照图像、停车类型识别、停车类型识别等方面的难题,并提出了一种新的基于计算机视觉的停车空间自动采集方法。通过对运动车辆和静止车辆进行分类,并对图像中的部分遮挡进行处理,通过不永久性地记录相同的环境,降低了监控风险,使停车容量估计具有可扩展性。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news reporting out of Leipzig, Germany, by N ewsRx editors, research stated, "The growing number of cars and limited street s pace present significant challenges for cities, applying not only to moving but extending to stationary traffic. The quest for parking spaces exacerbates traffi c congestion, noise, and air pollution, particularly in residential areas." Our news correspondents obtained a quote from the research from University of Le ipzig: "To develop effective parking solutions for these challenges, a trustful data foundation on available parking space capacities, its usage and parking typ e is crucial. Gathering this data is currently time-consuming, requiring manual labeling and street inspections. Moreover, it must be repeated to keep the data current. Research on parking space management has heavily focused on monitoring designated parking lots with fixed cameras to identify free or occupied parking spaces. However, due to privacy concerns fixed cameras are not applicable for th e larger part of the street space in European cities. This paper introduces a no vel computer visionbased method for automatically collecting parking space capa cities and parking type information. Our approach combines both street view and aerial imagery, which are recorded by a moving camera source. We tackle challeng es in geo-referencing images, identifying parking types, classifying moving and stationary cars and dealing with partial occlusions in images. By not permanentl y recording the same environment, our approach lowers the surveillance risk, mak ing parking capacity estimation scalable."
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