Cloud Enabled Tools for CamHD Visual Analysis

Aaron Marburg edited this page Jan 5, 2017 · 2 revisions


The publicly available data generated by CamHD have enormous scientific potential, with the capacity to support a wide range of geological, biological, hydrological, and oceanographic investigations using image analysis methods. However, the large size of the video archive and the lack of co-located computing infrastructure at the CI constitute a significant barrier to CamHD science. For end users, downloading this immense dataset for local analysis represents a significant burden in terms of time, bandwidth, and data storage costs. To fully realize the potential of the CamHD system for long-time-series investigations using image analysis, a co-located storage and computing solution must be developed.


  1. Develop a workflows and tools which make accessing and processing high-def (ProRes) CamHD video data less painful. Current pain points are the need to download full video files to extract video subsets locally and challenges in sharing or reusing video files once downloaded (other than adhoc methods).

The workflow should work at multiple levels: for the individual researcher, single computer lab, and in cloud compute/storage environment.

  1. Embed these tools in a video analysis tool which does image stitching (as a first step towards camera attitude estimation). Developed on the desktop but with a clear path to scaling up to the cloud.

Success Criteria

Tools of sufficient quality that collaborator (Tim) will use it.

Quantify performance metrics (time, bytes downloaded) for tools versus full file download for typical workloads (how are these defined?).

Fully documented (to current state) including HOW-TO for common use cases (individual user, deployment to cloud?)



Quantified performance results with tools and demonstration of existing image processing tools at that state.


Tools to usable state with basic deployment and scaling on cloud. Some science output from image processing tools, maybe not completely refined.

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