  Effectively measuring the similarity between two human motions is necessary
for several computer vision tasks such as gait analysis, person identi-
fication and action retrieval. Nevertheless, we believe that traditional
approaches such as L2 distance or Dynamic Time Warping based on hand-crafted
local pose metrics fail to appropriately capture the semantic relationship
across motions and, as such, are not suitable for being employed as metrics
within these tasks. This work addresses this limitation by means of a
triplet-based deep metric learning specifically tailored to deal with human
motion data, in particular with the prob- lem of varying input size and
computationally expensive hard negative mining due to motion pair alignment.
Specifically, we propose (1) a novel metric learn- ing objective based on a
triplet architecture and Maximum Mean Discrepancy; as well as, (2) a novel deep
architecture based on attentive recurrent neural networks. One benefit of our
objective function is that it enforces a better separation within the learned
embedding space of the different motion categories by means of the associated
distribution moments. At the same time, our attentive recurrent neural network
allows processing varying input sizes to a fixed size of embedding while
learning to focus on those motion parts that are semantically distinctive. Our
ex- periments on two different datasets demonstrate significant improvements
over conventional human motion metrics.
