Siamese Networks for Weakly Supervised Human Activity Recognition
Taoran Sheng Manfred Huber
Deep learning has been successfully applied to human activity recognition.
However, training deep neural networks requires explicitly labeled data which
is difficult to acquire. In this paper, we present a model with multiple
siamese networks that are trained by using only the information about the
similarity between pairs of data samples without knowing the explicit labels.
The trained model maps the activity data samples into fixed size representation
vectors such that the distance between the vectors in the representation space
approximates the similarity of the data samples in the input space. Thus, the
trained model can work as a metric for a wide range of different clustering
algorithms. The training process minimizes a similarity loss function that
forces the distance metric to be small for pairs of samples from the same kind
of activity, and large for pairs of samples from different kinds of activities.
We evaluate the model on three datasets to verify its effectiveness in
segmentation and recognition of continuous human activity sequences.