Anting Shen

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2016-193

December 8, 2016

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-193.pdf

We present our annotation tool for frame-by-frame bounding box annotation in videos. The tool has been used in conjunction with Amazon Mechanical Turk as well as standalone, to annotate datasets for Berkeley Deep Drive, BMW, DeepScale, and XYSense. Building upon ideas from previous works in this area, we present our improvements and optimizations on their user interfaces. We also introduce the idea of tuning such an annotation tool to reduce researcher’s friction, which we argue is just as important as streamlining a worker’s workflow due to the high cost of researcher time. We share our experiences with existing tools, and our ideas (and code) for how to make the experience better for researchers. We hope our findings and contributions reduce the cost of producing a labeled video dataset, and introduce ideas that will improve such annotation software in the future.

Advisors: Kurt Keutzer

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BibTeX citation:

@mastersthesis{Shen:EECS-2016-193,
    Author= {Shen, Anting},
    Title= {BeaverDam: Video Annotation Tool for Computer Vision Training Labels},
    School= {EECS Department, University of California, Berkeley},
    Year= {2016},
    Month= {Dec},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-193.html},
    Number= {UCB/EECS-2016-193},
    Abstract= {We present our annotation tool for frame-by-frame bounding box annotation in videos. The tool has been used in conjunction with Amazon Mechanical Turk as well as standalone, to annotate datasets for Berkeley Deep Drive, BMW, DeepScale, and XYSense. Building upon ideas from previous works in this area, we present our improvements and optimizations on their user interfaces. We also introduce the idea of tuning such an annotation tool to reduce researcher’s friction, which we argue is just as important as streamlining
a worker’s workflow due to the high cost of researcher time. We share our experiences with existing tools, and our ideas (and code) for how to make the experience better for
researchers. We hope our findings and contributions reduce the cost of producing a labeled video dataset, and introduce ideas that will improve such annotation software in the future.},
}

EndNote citation:

%0 Thesis
%A Shen, Anting 
%T BeaverDam: Video Annotation Tool for Computer Vision Training Labels
%I EECS Department, University of California, Berkeley
%D 2016
%8 December 8
%@ UCB/EECS-2016-193
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-193.html
%F Shen:EECS-2016-193