  The increasingly wide usage of location aware sensors has made it possible to
collect large volume of trajectory data in diverse application domains. Machine
learning allows to study the activities or behaviours of moving objects (e.g.,
people, vehicles, robot) using such trajectory data with rich spatiotemporal
information to facilitate informed strategic and operational decision making.
In this study, we consider the task of classifying the activities of moving
objects from their noisy indoor trajectory data in a collaborative
manufacturing environment. Activity recognition can help manufacturing
companies to develop appropriate management policies, and optimise safety,
productivity, and efficiency. We present a semi-supervised machine learning
approach that first applies an information theoretic criterion to partition a
long trajectory into a set of segments such that the object exhibits
homogeneous behaviour within each segment. The segments are then labelled
automatically based on a constrained hierarchical clustering method. Finally, a
deep learning classification model based on convolutional neural networks is
trained on trajectory segments and the generated pseudo labels. The proposed
approach has been evaluated on a dataset containing indoor trajectories of
multiple workers collected from a tricycle assembly workshop. The proposed
approach is shown to achieve high classification accuracy (F-score varies
between 0.81 to 0.95 for different trajectories) using only a small proportion
of labelled trajectory segments.
