Agent’s activity recognition: a focus on comparison of automatically-learned and hand-crafted features

摘要

Conventional machine learning methods use the selected hand-crafted features for activity recognition (AR). Motivated by the recent trends of AR applications in both human and robots using deep learning with automatically learned features, this paper explores to compare the performance of automatically-learned features by the deep networks and more comprehensive hand-crafted features with the conventional classification methods. We design and optimize the Recurrent Neural Networks (RNN) with the learned features and apply Support Vector Machine (SVM) and Random Forest (RF) with the completed hand-crafted features. We use two datasets for evaluation, i.e., a ground-truth dataset from human and a benchmark dataset from robots. The experimental results indicate that the learned features by deep networks and the hand-crafted features can perform equally well on the robot dataset; the hand-crafted features even perform better than the trained RNN networks on the ground-truth data.