  Pedestrian trajectory prediction for surveillance video is one of the
important research topics in the field of computer vision and a key technology
of intelligent surveillance systems. Social relationship among pedestrians is a
key factor influencing pedestrian walking patterns but was mostly ignored in
the literature. Pedestrians with different social relationships play different
roles in the motion decision of target pedestrian. Motivated by this idea, we
propose a Social Relationship Attention LSTM (SRA-LSTM) model to predict future
trajectories. We design a social relationship encoder to obtain the
representation of their social relationship through the relative position
between each pair of pedestrians. Afterwards, the social relationship feature
and latent movements are adopted to acquire the social relationship attention
of this pair of pedestrians. Social interaction modeling is achieved by
utilizing social relationship attention to aggregate movement information from
neighbor pedestrians. Experimental results on two public walking pedestrian
video datasets (ETH and UCY), our model achieves superior performance compared
with state-of-the-art methods. Contrast experiments with other attention
methods also demonstrate the effectiveness of social relationship attention.
