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Diffstat (limited to 'experimental.tex')
| -rw-r--r-- | experimental.tex | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/experimental.tex b/experimental.tex index a7871eb..dd5a73c 100644 --- a/experimental.tex +++ b/experimental.tex @@ -176,7 +176,7 @@ varying group size $n_p = \{3,5,10,25\}$. \fref{fig:offline} shows the precision-recall plot as the threshold varies. -Both algrithms perform three times better than the majority class baseline of +Both algorithms perform three times better than the majority class baseline of 15\% with a recall of 100\% on all people. We make two main observations. First, as expected, SHT performs better than MoG because of temporal smoothing. Second, performance is inversely proportional to group size. As we test @@ -364,7 +364,7 @@ given the relatively low resolution of the Kinect's infrared camera. \fref{fig:var} compares the precision-recall curve of \fref{fig:offline:sht} to the curve of the same experiment run on the newly obtained dataset. We observe -a roughly 20\% increase in performace across most thresholds. We +a roughly 20\% increase in performance across most thresholds. We believe these results would significantly outperform face recognition in a similar setting. |