  Trajectory prediction aims to forecast agents' possible future locations
considering their observations along with the video context. It is strongly
needed by many autonomous platforms like tracking, detection, robot navigation,
and self-driving cars. Whether it is agents' internal personality factors,
interactive behaviors with the neighborhood, or the influence of surroundings,
they all impact agents' future planning. However, many previous methods model
and predict agents' behaviors with the same strategy or feature distribution,
making them challenging to make predictions with sufficient style differences.
This paper proposes the Multi-Style Network (MSN), which utilizes style
proposal and stylized prediction using two sub-networks, to provide multi-style
predictions in a novel categorical way adaptively. The proposed network
contains a series of style channels, and each channel is bound to a unique and
specific behavior style. We use agents' end-point plannings and their
interaction context as the basis for the behavior classification, so as to
adaptively learn multiple diverse behavior styles through these channels. Then,
we assume that the target agents may plan their future behaviors according to
each of these categorized styles, thus utilizing different style channels to
make predictions with significant style differences in parallel. Experiments
show that the proposed MSN outperforms current state-of-the-art methods up to
10% quantitatively on two widely used datasets, and presents better multi-style
characteristics qualitatively.
