  Prediction with high accuracy is essential for various applications such as
autonomous driving. Existing prediction models are easily prone to errors in
real-world settings where observations (e.g. human poses and locations) are
often noisy. To address this problem, we introduce GPRAR, a graph convolutional
network based pose reconstruction and action recognition for human trajectory
prediction. The key idea of GPRAR is to generate robust features: human poses
and actions, under noisy scenarios. To this end, we design GPRAR using two
novel sub-networks: PRAR (Pose Reconstruction and Action Recognition) and FA
(Feature Aggregator). PRAR aims to simultaneously reconstruct human poses and
action features from the coherent and structural properties of human skeletons.
It is a network of an encoder and two decoders, each of which comprises
multiple layers of spatiotemporal graph convolutional networks. Moreover, we
propose a Feature Aggregator (FA) to channel-wise aggregate the learned
features: human poses, actions, locations, and camera motion using
encoder-decoder based temporal convolutional neural networks to predict future
locations. Extensive experiments on the commonly used datasets: JAAD [13] and
TITAN [19] show accuracy improvements of GPRAR over state-of-theart models.
Specifically, GPRAR improves the prediction accuracy up to 22% and 50% under
noisy observations on JAAD and TITAN datasets, respectively
