Cloud technology applications for area surveillance
Ryan Wu, Anna Deng, Yu Chen, Erik Blasch, Bingwei Liu · 2015
Efficient area surveillance in the Big Data era requires the capability of quickly abstracting useful information from the overwhelmingly increasing amount of data. Real-time information fusion is imperative and challenging to mission critical surveillance tasks for variant applications. Cloud computing has been recognized as an ideal candidate for Big Data because of many attractive features including high elasticity, good scalability, supporting pay-as-you-go service models, and capability of overcoming the constraints in both software parallelism and hardware capacities. In this work, we demonstrate that container-based virtualization outperforms the hypervisor-based Cloud Computing platforms. Taking WAMI (Wide-Area Motion Imagery), FMV (Full Motion Video), and text data as case studies, our experimental studies validate the advantages of container-based Cloud for area surveillance applications.