Exploiting the Nature of Repetitive Actions for Their Effective and Efficient Recognition

In the field of human action recognition (HAR), the recognition of actions with large duration is hindered by the memorization capacity limitations of the standard probabilistic and recurrent neural network (R-NN) approaches that are used for temporal sequence modeling. The simplest remedy is to employ methods that reduce the input sequence length, by performing window sampling, pooling, or key-frame extraction. However, due to the nature of the frame selection criteria or the employed pooling operations, the majority of these approaches do not guarantee that the useful, discriminative information is preserved. In this work, we focus on the case of repetitive actions. In such actions, a discriminative, core execution motif is maintained throughout each repetition, with slight variations in execution style and duration. Additionally, scene appearance may change as a consequence of the action. We exploit those two key observations on the nature of repetitive actions to build a compact and efficient representation of long actions by maintaining the discriminative sample information and removing redundant information which is due to task repetitiveness. We show that by partitioning an input sequence based on repetition and by treating each repetition as a discrete sample, HAR models can achieve an increase of up to 4% in action recognition accuracy. Additionally, we investigate the relation between the dataset and action set attributes with this strategy and explore the conditions under which the utilization of repetitiveness for input sequence sampling, is a useful preprocessing step in HAR. Finally, we suggest deep NN design directions that enable the effective exploitation of the distinctive action-related information found in repetitiveness, and evaluate them with a simple deep architecture that follows these principles.