  Video activity recognition by deep neural networks is impressive for many
classes. However, it falls short of human performance, especially for
challenging to discriminate activities. Humans differentiate these complex
activities by recognising critical spatio-temporal relations among explicitly
recognised objects and parts, for example, an object entering the aperture of a
container. Deep neural networks can struggle to learn such critical
relationships effectively. Therefore we propose a more human-like approach to
activity recognition, which interprets a video in sequential temporal phases
and extracts specific relationships among objects and hands in those phases.
Random forest classifiers are learnt from these extracted relationships. We
apply the method to a challenging subset of the something-something dataset and
achieve a more robust performance against neural network baselines on
challenging activities.
