We developed a software system to extract and track vehicle license plate numbers from real-time surveillance cameras and crowd sourced video streams. The system can also calculate the probable routes of a vehicle over a range of dates based on the geographical coordinates. In this paper, we present both of our linear and parallel processing implementation schemes and analyze the performance based on evaluation results. Our results show that while cloud based parallel processing can address the scalability needs, performance outweighs the cost only when the real-time streaming data becomes increasingly large.
video surveillance, parallel processing, apache storm, Microsoft Azure, Google Cloud, HDInsight
Iqbal, Razib, Matthew Kenney, and Jamil Saquer. "Should We Place the License Plate Tracking System in the Cloud?." In Proceedings of the 14th International Joint Conference on e-Business and Telecommunications, pp. 77-80. 2017.