Figure 6.

uulmMAD classification performance. The proposed network was trained on the key pose frames extracted from the uulmMAD action recognition dataset. (A) Shows the per class classification rates obtained by single key pose frame classification. This allows the recognition of an action in the instant a key pose frame emerges in the input sequence. Average classwise recall (on the diagonal) ranges from 0.78 to 0.98. Some of the notable confusions between classes can be explained by a large visual similarity (e.g., between ED2 and ST4). In (B) sequence level majority voting was applied. The final decision is made after evaluating all key pose frames within an action sequence and determining the class with the most frequent occurrence of key poses. The resulting per class values of Rec_M range from 0.94 to 1.00. A sliding window based classification scheme was evaluated in (C). The best and worst per class average recall values together with the average value of Rec_M are displayed for temporal window sizes from 1 to 60 frames. In addition, the percentage of windows containing one or more key pose frames (and thus allow a classification of the action) is shown (blue line).