A Comparison of Few-Shot Classification of Human Movement Trajectories

Zusammenfassung (Abstract) :

In the active research area of human action recognition, a lot of different approaches to classify behavior have been proposed and evaluated. However, evaluations on movement recognition with a limited number of training examples, also known as Few-shot classification, are rare. In many applications, the generation of labeled training data is expensive. Manual efforts can be reduced if algorithms are used which give reliable results on small datasets. In this paper, three recognition methods are compared on gesture and stick-throwing movements of different complexity performed individually without detailed instructions in experiments in which the number of the examples used for training is limited. Movements were recorded with markerbased motion capture systems. Three classification algorithms, the Hidden Markov Model, Long Short-Term Memory network and k-Nearest Neighbor, are compared on their performance in recognition of these arm movements. The methods are evaluated regarding accuracy with limited training data, computation time and generalization to different subjects. The best results regarding training with a small number of examples and generalization are achieved with LSTM classification. The shortest calculation times are observed with k-NN classification, which shows also very good classification accuracies on data of low complexity.

Stichworte :

Few-shot Learning, Movement Recognition, Human Movement Analysis, k-Nearest Neighbor, Long Short- Term Memory, Hidden Markov Model.