Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning and Statistical Models for Time-Critical Pedestrian
  Behaviour Prediction   The time it takes for a classifier to make an accurate prediction can be
crucial in many behaviour recognition problems. For example, an autonomous
vehicle should detect hazardous pedestrian behaviour early enough for it to
take appropriate measures. In this context, we compare the switching linear
dynamical system (SLDS) and a three-layered bi-directional long short-term
memory (LSTM) neural network, which are applied to infer pedestrian behaviour
from motion tracks. We show that, though the neural network model achieves an
accuracy of 80%, it requires long sequences to achieve this (100 samples or
more). The SLDS, has a lower accuracy of 74%, but it achieves this result with
short sequences (10 samples). To our knowledge, such a comparison on sequence
length has not been considered in the literature before. The results provide a
key intuition of the suitability of the models in time-critical problems.

31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
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The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex