\textbf{Unsolved Problem 1}: No fully automatic solution today is capable
of detecting and tracking both the people and the basketball.

\textbf{Unsolved Problem 2}: Building large video data sets is inefficient because
frame-by-frame hand labeling is slow, costly, and tedious. 

\vspace{0.5cm}

\section{Motivations}
\begin{enumerate}
    \item What is the best division of labor for crowdsource video labeling?
    \item What are the tradeoffs between automation and manual labeling?
    \item Given a fixed budget, what is the best accuracy we can achieve?
\end{enumerate}

\section{Contributions}
\begin{itemize}
    \item A set of ``best-practices'' for crowdsourced video annotation.
    \item In contrast to \cite{yuen-labelme}, can interpolate nonlinear paths w/o much effort.
    \item Expanding \cite{vijayanarasimhan2009s} to analyze tradeoffs between human and CPU cost.
    \item Ability to build \emph{massive} video data sets under a budget.
    \item A reusable, open source video annotation platform for affordable, research
    video labeling.
\end{itemize}

\section{Mechanical Turk}
\begin{itemize}
\item Mechanical Turk: online, monetized, crowdsourced marketplace.
\item Ideal for tasks that are hard for computers, but trivial for humans.
\item Workers complete \textit{Human Intelligence Tasks} and we get results.
\end{itemize}

\section{The ``Turk Philosophy''}
\begin{itemize}
\item Suggests completely replacing automation with human effort.
\item For \emph{Images}: annotate every object (highly successful). \cite{sorokin51utility}
\item For \emph{Video}: hand label every frame (highly inefficient).
\end{itemize}

\begin{itemize}
\item Given the redundant yet dynamic nature of video, we need an approach
that combines the computational power of the CPU with the superior vision capability of humans.
\end{itemize}

\section{References}
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\bibliography{refs.bib}
