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Diffstat (limited to 'experimental.tex')
| -rw-r--r-- | experimental.tex | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/experimental.tex b/experimental.tex index d03763b..8085321 100644 --- a/experimental.tex +++ b/experimental.tex @@ -47,7 +47,7 @@ outputs a set of joints in real world coordinates. The view of the Kinect is seen in \fref{fig:hallway}, showing the color image, the depth image with figures, and the fitted skeleton of a person in a single frame. Skeletons are fit from roughly 1-5 meters away from the Kinect. For each frame with a -skelton we record color image and the positions of the joints. +skeleton we record color image and the positions of the joints. \begin{figure}[t] \begin{center} @@ -232,14 +232,14 @@ building. %run. We only evaluate SHT in this setting since it already takes consecutive frames into account and because it performed better than MoG in the offline setting -(\ref{sec:experiment:offline}). We partition the dataset into 10 time +(\xref{sec:experiment:offline}). We partition the dataset into 10 time sequences of equal size. For a given threshold, the algorithm is trained and tested incrementally: train on the first $k$ sequences (in the chronological order) and test on the $(k+1)$-th sequence. \fref{fig:online} shows the prediction-recall curve when averaging the prediction rate over the 10 incremental experiments. Overall performance is worse than in \fref{fig:offline:sht} since the system trains on less data than in -\ref{sec:experiment:offline} in all but the last step. We still see +\xref{sec:experiment:offline} in all but the last step. We still see recognition rates mostly above 90\% for group sizes of 3 and 5. \begin{figure}[t] |