The argumentation in other words would show us that essence can be reduced down to a variation of appearances (involving the roles of memory and anticipation).
However, such action recognition systems still suffer from problems depending on variations of appearance.
Figure 1 (a) Variations of appearance due to different clothes in the same action.
Thus, it can be employed to cope with variations of appearance for the accurate action recognition.
We discuss how careful modeling of the variations of appearance and motion leads to improved biometric systems.
For example, two subimages of the same action obtained by resizing AMIs are shown in Figure 4, which have variations of appearance due to different clothes and backpack.
However, most of them are apt to fail in tolerating variations of appearance due to the clothes and objects carried by actors, which is often observed in surveillance environments.
We propose an action recognition scheme based on the ordinal measure of accumulated motion, which is robust to variations of appearances.
This paper by Kim et al., entitled "Human action recognition using ordinal measure of accumulated motion," presents a robust method for recognizing human actions from a single query action video with variations of appearances.
An experimental setup using VR would also allow for the systematic variation of the appearance of avatars and environmental features, e.g., lighting, presence of others, type of bar, and assess their specific influence.
However, considering the variation of object appearance, it is reasonable to argue that the feature comparison based on the sum of few randomly selected pixels is probably more robust than the conventional pixel-wise comparison.
