Human-Object Maps for Daily Activity Recognition

In the field of action recognition, when and where an interaction between a human and an object happens has the potential to be valid information in enhancing action recognition accuracy. Especially, in daily life where each activities are performed in longer time frame, conventional short term action recognition may fail to generalize do to the variety of shorter actions that could take place during the activity. In this paper, we propose a novel representation of human object interaction called Human-Object Maps (HOMs) for recognition of long term daily activities. HOMs are 2D probability maps that represents spatio-temporal information of human object interaction in a given scene. We analyzed the effectiveness of HOMs as well as features relating to the time of the day in daily activity recognition. Since there are no publicly available daily activity dataset that depicts daily routines needed for our task, we have created a new dataset that contains long term activities. Using this dataset, we confirm that our method enhances the prediction accuracy of the conventional 3D ResNeXt action recognition method from 86.31% to 97.89%.