## 4 Experimental Conditions and Results
The highest accuracy for actions performed in place is 100% if MBR width is used and the action representation contains 54 elements. It means that each action sequence, regardless of the number of frames, is represented using 54 Fast Fourier Transform coefficients and a representation has a form of a vector with 54 real values. The matching process can be then performed using Euclidean distance or C1 correlation. In total, 100% accuracy is also obtained for X/Y Feret, but more DFT coefficients are required. Actions with changing location of a silhouette are most successfully classified if MBR area is used and action representation contains 32 values--an accuracy of 94.44% is yielded. Again, either Euclidean distance or C1 correlation can be employed. Ultimately, the averaged correct classification rate for exercise types recognition is 97.2% (8 action classes). A second set of experiments concerned the use of the Weizmann database as a benchmark and the comparison of the results for 10 classes with the previous version of our approach, presented in [29]. The results are presented in Table 2. For actions performed in place the highest accuracy is 86.67% (MBR perimeter, 52 DFT coefficients, Euclidean distance) and for actions with changing location of a silhouette the accuracy equals 95.56% (MBR area, 33 DFT coefficients, C1 correlation). If the use of different methods and parameters for each subgroup is assumed, the averaged accuracy for the entire database is 91.12%. This outperforms our previous approach based on simple shape descriptors, that resulted in 83.3% accuracy for actions performed in place (MBR width) and 85.4% (PAM eccentricity) for the other subgroup. The averaged accuracy equalled then 84.35%, which means that the current approach improves the accuracy by nearly 7%. \begin{table}
\begin{tabular}{c c c} \hline \hline
**8 Classes** & **Actions Performed in Place** & **Actions with Changing Location of a Silhouette** \\ \hline Euclidean distance & 100.00\% & 94.44\% \\  & MBR width & MBR area \\  & (54) & (32) \\ \hline Correlation Coefficient & 97.22\% & 83.33\% \\  & circle variance & MBR perimeter \\  & (48) & (35) \\ \hline C1 Correlation & 100.00\% & 94.44\% \\  & MBR width & MBR area \\  & (54) & (32) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results for the recognition of exercise types using 8 classes of the Weizmann dataset. The results are presented separately for actions performed in place and actions with changing location of a silhouette. The highest accuracy values are listed with the indication of the applied simple shape descriptor and the size of an action representation (given in brackets). \begin{table}
\begin{tabular}{c c c} \hline \hline
**10 Classes** & **Actions Performed in Place** & **Actions with Changing Location of a Silhouette** \\ \hline Euclidean distance & 86.67\% & 91.11\% \\  & MBR perimeter & MBR area \\  & (52) & (32) \\ \hline Correlation Coefficient & 86.67\% & 84.44\% \\  & width/length ratio & perimeter and ellipse variance \\  & (53) & (66) \\ \hline C1 Correlation & 82.22\% & 95.56\% \\  & MBR perimeter & MBR area \\  & (56) & (33) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results for the Weizmann dataset used as a benchmark, presented separately for actions performed in place and actions with changing location of a silhouette. The highest accuracy values are listed with the indication of the applied simple shape descriptor and the size of an action representation (given in brackets).