Trajectory prediction aims at forecasting agents' possible future locations
considering their observations along with the video context. It is strongly
required by a lot of autonomous platforms like tracking, detection, robot
navigation, self-driving cars, and many other computer vision applications.
Whether it is agents' internal personality factors, interactive behaviors with
the neighborhood, or the influence of surroundings, all of them might represent
impacts on agents' future plannings. However, many previous methods model and
predict agents' behaviors with the same strategy or the ``single'' feature
distribution, making them challenging to give predictions with sufficient style
differences. This manuscript proposes the Multi-Style Network (MSN), which
utilizes style hypothesis and stylized prediction two sub-networks, to give
agents multi-style predictions in a novel categorical way adaptively. 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 a series of style channels in the network. Then, we assume one
by one that the target agents will plan their future behaviors according to
each of these categorized styles, thus utilizing different style channels to
give a series of predictions with significant style differences in parallel.
Experiments show that the proposed MSN outperforms current state-of-the-art
methods up to 10\% - 20\% quantitatively on two widely used datasets, and
presents better multi-style characteristics qualitatively