  This article proposes a novel attention-based body pose encoding for human
activity recognition that presents a enriched representation of body-pose that
is learned. The enriched data complements the 3D body joint position data and
improves model performance. In this paper, we propose a novel approach that
learns enhanced feature representations from a given sequence of 3D body
joints. To achieve this encoding, the approach exploits 1) a spatial stream
which encodes the spatial relationship between various body joints at each time
point to learn spatial structure involving the spatial distribution of
different body joints 2) a temporal stream that learns the temporal variation
of individual body joints over the entire sequence duration to present a
temporally enhanced representation. Afterwards, these two pose streams are
fused with a multi-head attention mechanism. % adapted from neural machine
translation. We also capture the contextual information from the RGB video
stream using a Inception-ResNet-V2 model combined with a multi-head attention
and a bidirectional Long Short-Term Memory (LSTM) network. %Moreover, we whose
performance is enhanced through the multi-head attention mechanism. Finally,
the RGB video stream is combined with the fused body pose stream to give a
novel end-to-end deep model for effective human activity recognition.
