  A human action can be seen as transitions between one's body poses over time,
where the transition depicts a temporal relation between two poses. Recognizing
actions thus involves learning a classifier sensitive to these pose transitions
as well as to static poses. In this paper, we introduce a novel method called
transitions forests, an ensemble of decision trees that both learn to
discriminate static poses and transitions between pairs of two independent
frames. During training, node splitting is driven by alternating two criteria:
the standard classification objective that maximizes the discrimination power
in individual frames, and the proposed one in pairwise frame transitions.
Growing the trees tends to group frames that have similar associated
transitions and share same action label incorporating temporal information that
was not available otherwise. Unlike conventional decision trees where the best
split in a node is determined independently of other nodes, the transition
forests try to find the best split of nodes jointly (within a layer) for
incorporating distant node transitions. When inferring the class label of a new
frame, it is passed down the trees and the prediction is made based on previous
frame predictions and the current one in an efficient and online manner. We
apply our method on varied skeleton action recognition and online detection
datasets showing its suitability over several baselines and state-of-the-art
approaches.
