Temporal spotting of human actions from videos containing actor's unintentional motions

This paper proposes a method for temporal action spotting: the temporal segmentation and classification of human actions in videos. Naturally performed human actions often involve actor's unintentional motions. These unintentional motions yield false visual evidences in the videos, which are not related to the performed actions and degrade the performance of temporal action spotting. To deal with this problem, our proposed method empolys a voting-based approach in which the temporal relation between each action and its visual evidence is probabilistically modeled as a voting score function. Due to the approach, our method can robustly spot the target actions even when the actions involve several unintentional motions, because the effect of the false visual evidences yielded by the unintentional motions can be canceled by other visual evidences observed with the target actions. Experimental results showed that the proposed method is highly robust to the unintentional motions.