Abstract-One of the greatest technological improvements in recent years is the rapid progress using machine learning for processing visual data. Among all factors that contribute to this development, datasets with labels play crucial roles. Several datasets are widely reused for investigating and analyzing different solutions in machine learning. Many systems, such as autonomous vehicles, rely on components using machine learning for recognizing objects. This paper compares different visual datasets and frameworks for machine learning. The comparison is both qualitative and quantitative and investigates object detection labels with respect to size, location, and contextual information. This paper also presents a new approach creating datasets using real-time, geo-tagged visual data, greatly improving the contextual information of the data. The data could be automatically labeled by cross-referencing information from other sources (such as weather).
Millions of cameras are openly connected to the Internet for a variety of purposes. This paper takes advantage of this resource to gather visual data. This camera data could be used for a myriad of purposes by solving two problems. (i) The network camera image data needs context to solve real world problems. (ii) While contextual data is available, it is not centrally aggregated. The goal is to make it easy to leverage the vast amount of network cameras. The database allows users to aggregate camera data from over 119,000 network camera sources all across the globe in real time. This paper explains how to collect publicly available information from network cameras. The paper describes how to analyze websites to retrieve relevant information about the cameras and to calculate the refresh rates of the cameras.
Millions of network cameras have been deployed worldwide. Real-time data from many network cameras can offer instant views of multiple locations with applications in public safety, transportation management, urban planning, agriculture, forestry, social sciences, atmospheric information, and more. This paper describes the real-time data available from worldwide network cameras and potential applications. Second, this paper outlines the CAM 2 System available to users at https: //www.cam2project.net/. This information includes strategies to discover network cameras and create the camera database, user interface, and computing platforms. Third, this paper describes many opportunities provided by data from network cameras and challenges to be addressed.
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