  A car driver knows how to react on the gestures of the traffic officers.
Clearly, this is not the case for the autonomous vehicle, unless it has road
traffic control gesture recognition functionalities. In this work, we address
the limitation of the existing autonomous driving datasets to provide learning
data for traffic control gesture recognition. We introduce a dataset that is
based on 3D body skeleton input to perform traffic control gesture
classification on every time step. Our dataset consists of 250 sequences from
several actors, ranging from 16 to 90 seconds per sequence. To evaluate our
dataset, we propose eight sequential processing models based on deep neural
networks such as recurrent networks, attention mechanism, temporal
convolutional networks and graph convolutional networks. We present an
extensive evaluation and analysis of all approaches for our dataset, as well as
real-world quantitative evaluation. The code and dataset is publicly available.
