# Action Classification for Partially Occluded Silhouettes by Means of Shape and Action Descriptors
## 5 Discussion and Conclusions  
This paper presents an approach for action recognition in the scenario of exercise classification under partial occlusion. It uses a combination of shape descriptors, trajectory and spectral domain features to assign silhouette sequences to action classes. General shape features are tracked; therefore, small disturbances or artefacts in the foreground masks are of minor importance. The proposed approach has a form of a general procedure consisting of several processing steps, but some of them are using different methods. It enables this approach to be adapted to other applications or data. It also made it possible to test different combinations of shape descriptors, action descriptors and matching measures in the assumed scenario. In order to improve recognition accuracy, applying other shape features or classification procedures is considered. The goal of the experiments was to indicate such combination that would give the highest accuracy for the recognition of occluded silhouettes. Results differ between sub  
Figure 5: Confusion matrices for the best experiment using the ‘lower-occlusion’ database. (**Left table**): ‘actions performed in place’ subgroup, Elongation, C1 correlation, 55 DFT coefficients, accuracy \(91.11\%\). (**Right table**): ‘actions with changing location of a silhouette’ subgroup, ShorterMBR, C1 correlation, 71 DFT coefficients, accuracy 73.33%. Figure 6: Confusion matrices for the best experiment using the ‘upper-occlusion’ database. (**Left table**): ‘actions performed in place’ subgroup, Elongation, C1 correlation, 51 DFT coefficients, accuracy \(88.89\%\). (**Right table**): ‘actions with changing location of a silhouette’ subgroup, Rectangularity, C1 correlation, 46 DFT coefficients, accuracy \(84.44\%\). Figure 7: Confusion matrices for the best experiment using the original database without occlusions. (**Left table**): ‘actions performed in place’ subgroup, Perimeter, C1 correlation, 51 DFT coefficients, accuracy \(91.11\%\). (**Right table**): ‘actions with changing location of a silhouette’ subgroup, Area, Euclidean distance, 31 DFT coefficients, accuracy \(86.67\%\). groups of actions performed in place and actions with changing location of a silhouette. For the 'lower-occlusion' database, the highest accuracy is 91.11% (elongation, 55 coefficients, C1 correlation) and 73.33% (shorterMBR, 71 coefficients, C1 correlation), for the subgroups, respectively. In turn, in the 'upper-occlusion' database, the highest accuracy in the first subgroup is 88.89% (elongation, 51 coefficients, C1 correlation) and 84.44% in the second one (rectangularity, 46 coefficients, C1 correlation). This gives an average accuracy for the entire 'upper-occlusion' database equal to 86.65%, while, for the 'lower-occlusion' database, it is 82.22%. Compared to the averaged accuracy in data without occlusions, which is 88.89%, the decrease in classification rate is small. It can also be concluded that classification accuracy for actions performed in place is close to 90%, and higher than in the other subgroup, which makes the results promising, especially under the assumed scenario. The proposed approach was tested using the datasets modified by the authors; therefore, there are no other results in the literature, presented on the same data. However, some general characteristics of the results can be compared to the conclusions on some other solutions handling occlusion. The authors of [35] investigated combinations of different descriptors as well. They experimented with partial occlusion (10% or 25% of the region of interest is occluded) and heavy occlusion (50% and 75% occluded), and compared the results with unoccluded data. The percentage results are similar to the results presented in this paper. For the experiments combining trajectory with MBH or HOG, the difference in accuracy between partially occluded and unoccluded data amounted to several percent.