使用有限的訓練資料與分層式搜尋之手部動作追蹤技術

Abstract

In this thesis, we consider tracking an articulated hand without using markers. Our hand tracking method performs nearest-neighbor-based search in a 3D hand model large database. For robustly and efficiently, we choose to capture a small real hand images database for each user as an intermediate dataset. And use Hierarchical-searching and temporal consistency to efficiently search in the large database and disambiguate the result. Our prototype system can estimate hand pose including rigid and non-rigid out-of-image-plane rotation, slow and fast gesture charging when rotation, and recover after the hand left the camera in real time. We believe it can be a more intuitive way for advance human computer interaction.

In this thesis, we consider tracking an articulated hand without using markers. Our hand tracking method performs nearest-neighbor-based search in a 3D hand model large database. For robustly and efficiently, we choose to capture a small real hand images database for each user as an intermediate dataset. And use Hierarchical-searching and temporal consistency to efficiently search in the large database and disambiguate the result. Our prototype system can estimate hand pose including rigid and non-rigid out-of-image-plane rotation, slow and fast gesture charging when rotation, and recover after the hand left the camera in real time. We believe it can be a more intuitive way for advance human computer interaction.