Highly Robust Action Retrieval using View-invariant Pose Feature and Simple yet Effective Query Expansion Method

Abstract

Action retrieval and detection utilizing view-invariant pose based feature achieve high precision. However the technology has a problem of low recall because of the large individual differences in action. Query-expansion(QE) methods are well known as effective ways to improve recall in object detection and retrieval task, but few research adapt it to the action retrieval task. We focused on the query expansion method and proposed new query generation method in which two queries containing missing points complement each other's missing points to perform high-recall action retrieval. The experimental results are reported to show that our method outperforms the state-of-the-art methods in a simulated dataset with annotated multi-view 2D poses and a real-world video dataset.