# Action Classification for Partially Occluded Silhouettes by Means of Shape and Action Descriptors
## 4 Experimental Conditions and Results  
The experiments were carried out with the use of the Weizmann database [21], which contains ten action classes with two action types: the first type includes actions performed in place, such as bending (referred to as 'bend'), jumping jack ('jack'), jumping in place ('pjump'), waving with one hand ('wave1') and waving with two hands ('wave2'), and the second type refers to actions with changing location of a silhouette, namely walking ('walk'), running ('run'), galloping sideways ('side'), jumping forward on two legs ('jump') and skipping ('skip'). Action sequences are divided into subgroups during coarse classification and the division is made based on the trajectory length; therefore, the subgroups are referred to as'short trajectory' and 'long trajectory', respectively. All foreground images from 90 sequences were preprocessed in accordance with Section 3.1. Based on Section 3.2, two versions of the database were prepared by occluding the upper or lower part of the silhouette, and are referred to as the 'upper-occlusion' and 'lower-occlusion'. Each database is tested separately. In a single experiment, there are several tests investigating classification accuracy of the combinations of the shape descriptor, matching measure and the size of action representation. A sequence of binary silhouettes (less than 150 images per action) is taken as an input data for a single action. Firstly, all silhouettes are represented using selected shape descriptor and the results are combined into a vector which is then normalized. Vectors from all silhouette sequences are transformed into action representations that are subjected to the classification process. This includes coarse classification into two subgroups and final classification into action classes using leave-one-sequence-out procedure. A test representation is matched with the rest of the representations and the most similar one indicates the probable action class. Two matching measures are compared: Euclidean distance and C1 correlation. The experiments employed nine shape descriptors and various action representations based on from 2 to 256 DFT coefficients. The best result relates to the experiment with the highest accuracy (correct classification rate) and the smallest representation size. Then, a combination of a shape descriptor and matching measure that is used during this experiment is considered as the most effective version of the approach. The top results for each shape descriptor, obtained for occluded data, are presented in Tables 1 and 2. In turn, Table 3 contains a comparison of the selected results achieved using both occluded databases and the original [PERSON] database. Table 1 contains the results of the experiments performed on the 'lower-occlusion' database. The highest classification accuracy for actions performed in place is 91.11%. The second best is 88.89%, and the third one is 84.44%. The most accurate experiment employed elongation for shape description. The accuracy of 91.11% is then achieved either for Euclidean distance or C1 correlation, and action representations are composed of 57 and 55 coefficients, respectively. For the second group of actions, the maximum recognition rate is 73.33% and was obtained for two shape descriptors--rectangularity (84 coefficients) and shorterMBR (71 coefficients). In both experiments, the C1 correlation is applied. The difference between the highest results in both subgroups is large and equals nearly 20%. In case of actions with changing location of a silhouette, this should not be surprising since the lower part of the silhouette is occluded and feet cannot be localised continuously. The results for the experiments performed with the use of the 'upper-occlusion' database are not so strongly diversified between the subgroups (Table 2). The highest classification rate for actions performed in place equals 88.89% and is attributed to the experiment using elongation, 51 Fourier coefficients and C1 correlation.