\section{Qualitative Experiments}
\label{sec:experiments}

In order to demonstrate our framework's capabilities, we show how our approach
handles a couple of interesting annotation problems.  We have assembled two
data sets: a synthetic video of easy-to-localize rectangles maneuvering in an
uncluttered background, and a real-world data set of actors following scripted
walking patterns.
%The purpose of these data sets is not to compare against the
%state-of-the-art (we do that in the next section), but rather offer an intuitive understanding of our active
%learning method.

We refer the reader to the figures.
Fig.\ref{fig:bounce} shows how our framework is able to resolve
inherently ambiguous motion with the minimum number of annotations.
Fig.\ref{fig:straightslide} highlights how our framework does not request
annotations when the paths of two identical objects are disjoint because
the motion is not ambiguous.
Fig.\ref{fig:cluttered} reveals how our framework will gracefully degrade to
fixed rate key frames if the tracked object is difficult to localize.
Fig.\ref{fig:yi} demonstrates motion of objects that deform.
Fig.\ref{fig:occlusion} shows how we are able to detect occlusions and
automatically recover by querying for a correct annotation. Finally,
Fig.\ref{fig:stationary} shows how we are able to transfer wasted clicks from
stationary objects on to moving objects.

%\subsection{Inherently Ambiguous Motion}
%
%We first consider motion that is ambigious, a situation that can arise when
%multiple objects have an identical appearance. Even when the appearance model
%is perfect, the tracker will be inherently confused and will be forced to
%choose one particular path, even if it is the wrong path.  This case can occur
%in real world video: e.g., a set of identical twins, automobiles of the same
%color and make, or even pedestrians in low resolution video. In such
%situations, an oracle may be able to resolve the ambiguity by relying on cues
%other than observable phenomena, such as intimate domain knowledge.
%
%We analyze this case in Fig.\ref{fig:bounce} with our black boxes data set,
%which mimics the scenario with a perfect appearance model. In such cases, our
%active learning framework is optimal and can resolve paths in fewer clicks than
%a traditional fixed-rate tracker. We refer the reader to the caption in
%Fig.\ref{fig:bounce} for further details.
%
%\subsection{Deforming Motion}
%
%We next consider motion of an object that undergoes a significant appearance
%change, such as a nonrigid object deforming or a person changing his or her
%pose. Our framework is able to identify when the tracker loses the path due to
%this appearance change. We provide analysis of this situation in
%Fig.\ref{fig:yi} on our scripted walking data set. 
%
%\subsection{Occluded Motion}
%
%We now analyze an object that enters a total occlusion and reappears later,
%such as a person entering and leaving a building. Our framework is able to
%identify when the object is not visible and will query for annotations during
%the occlusion. We present this discussion in Fig.\ref{fig:occlusion}.
%
%\subsection{Stationary Motion}
%
%
