Date of Award

6-9-2019

Document Type

Thesis - SCU Access Only

Publisher

Santa Clara : Santa Clara University, 2019

Department

Computer Engineering

First Advisor

Ying Liu

Abstract

Future Internet-of-Things (IoT) will be featured by ubiquitous and pervasive vision sensors that generate enormous amount of streaming videos. The ability to analyze the big video data in a timely manner is essential to delay-sensitive applications, such as autonomous vehicles and body-worn cameras for police forces. Due to the limitation of computing power and storage capacity on local devices, the fog computing paradigm has been developed in recent years to process big sensor data closer to the end users while it avoids the transmission delay and huge uplink bandwidth requirements in cloud-based data analysis. In this work, we propose an edge-to-fog computing framework for object detection from surveillance videos. Videos are captured locally at an edge device and sent to fog nodes for colorassisted L1-subspace background modeling. The results are then sent back to the edge device for data fusion and final object detection. Experimental studies demonstrate that the proposed color-assisted background modeling offers more diversity than pure luminance based background modeling and hence achieves higher object detection accuracy. Meanwhile, the proposed edge-to-fog paradigm leverages the computing resources on multiple platforms. [1]

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