ACM Transactions on Internet Technology
Association for Computing Machinery
Reduction in the cost of Network Cameras along with a rise in connectivity enables entities all around the world to deploy vast arrays of camera networks. Network cameras offer real-time visual data that can be used for studying traffic patterns, emergency response, security, and other applications. Although many sources of Network Camera data are available, collecting the data remains difficult due to variations in programming interface and website structures. Previous solutions rely on manually parsing the target website, taking many hours to complete. We create a general and automated solution for aggregating Network Camera data spread across thousands of uniquely structured webpages. We analyze heterogeneous webpage structures and identify common characteristics among 73 sample Network Camera websites (each website has multiple web pages). These characteristics are then used to build an automated camera discovery module that crawls and aggregates Network Camera data. Our system successfully extracts 57,364 Network Cameras from 237,257 unique web pages.
Dailey, Ryan; Chawla, Aniesh; Liu, Andrew; Mishra, Sripath; Zhang, Ling; Majors, Josh; Lu, Yung-Hisang; and Thiruvathukal, George K.. Automated Discovery of Network Cameras in Heterogeneous Web Pages. ACM Transactions on Internet Technology, 22, 1: 1-25, 2021. Retrieved from Loyola eCommons, Computer Science: Faculty Publications and Other Works, http://dx.doi.org/10.1145/3450629
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© The Authors, 2021
Author Posting. © The Authors, 2021. This is the author's version of the work. It is posted here by permission of the Association for Computing Machinery for personal use, not for redistribution. The definitive version was published in ACM Transactions on Internet Technology, Volume 22, Issue 1, February 2022. https://doi.org/10.1145/3450629