Active learning for human pose estimation based on temporal pose continuity

Abstract

In recent years, human pose estimation based on deep learning has been actively studied for various applications. A large amount of training data is required to achieve good performance, but, annotating human poses is quite an expensive task. Therefore, there is a growing need to improve the efficiency of training data preparation. In this paper, we take an active learning approach to reduce the cost of preparing training data for human pose estimation. We propose an active learning method that automatically selects images effective for improving the performance of a human pose estimation model from unlabeled image sequences, focusing on the fact that the human pose continuously changes between adjacent frames in an image sequence. Specifically, by comparing the estimated human poses between frames, we select images incorrectly estimated as candidates for manual annotation. Then, the human pose estimation model is re-trained by adding a small portion of manually annotated data as training data. Through experiments, we confirm that the proposed method can effectively select training data candidates from unlabeled image sequences, and that the proposed method can improve the performance of the model with reducing the cost of manual annotations.