  Traffic violation and the flexible and changeable nature of pedestrians make
it more difficult to predict pedestrian behavior or intention, which might be a
potential safety hazard on the road. Pedestrian motion state (such as walking
and standing) directly affects or reflects its intention. In combination with
pedestrian motion state and other influencing factors, pedestrian intention can
be predicted to avoid unnecessary accidents. In this paper, pedestrian is
treated as non-rigid object, which can be represented by a set of
two-dimensional key points, and the movement of key point relative to the torso
is introduced as micro motion. Static and dynamic micro motion features, such
as position, angle and distance, and their differential calculations in time
domain, are used to describe its motion pattern. Gated recurrent neural network
based seq2seq model is used to learn the dependence of motion state transition
on previous information, finally the pedestrian motion state is estimated via a
softmax classifier. The proposed method only needs the previous hidden state of
GRU and current feature to evaluate the probability of current motion state,
and it is computation efficient to deploy on vehicles. This paper verifies the
proposed algorithm on the JAAD public dataset, and the accuracy is improved by
11.6% compared with the existing method.
