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Characterization of Energy and Performance Bottlenecks in an Omni-directional Camera System

Abstract Generating real-world content for VR is challenging in terms of capturing and processing at high resolution and high frame-rates. The content needs to represent a truly immersive experience, where the user can look around in 360-degree view and perceive the depth of the scene. The existing solutions only capture and offload the compute load to the server. But offloading large amounts of raw camera feeds takes longer latencies and poses difficulties for real-time applications. By capturing and computing on the edge, we can closely integrate the systems and optimize for low latency. However, moving the traditional stitching algorithms to battery constrained device needs at least three orders of magnitude reduction in power. We believe that c... (more)
Created Date 2018
Contributor Gunnam, Sridhar (Author) / LiKamWa, Robert (Advisor) / Turaga, Pavan (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Subject Electrical engineering / Computer engineering / Computer science / 360 camera systems / Computer Vision / Embedded Systems / Image Signal Processing / Mobile Systems / Omnidirectional Camera
Type Masters Thesis
Extent 33 pages
Language English
Note Masters Thesis Electrical Engineering 2018
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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