Directional chamfer matching in 2.5 dimensions

Authors

Prabhu Kaliamoorthi, Nanyang Technological University
Ramakrishna Kakarala, University of Wollongong

RIS ID

84213

Publication Details

Kaliamoorthi, P. & Kakarala, R. (2013). Directional chamfer matching in 2.5 dimensions. IEEE Signal Processing Letters, 20 (12), 1151-1154.

Abstract

Directional chamfer matching (DCM) has shown good results in many areas such as object recognition and pose estimation. Currently DCM has been applied only for two-dimensional (2-D) matching. In this letter, we present a DCM scheme that utilizes depth in addition to 2-D input, which we refer to as 2.5D DCM. We show that in situations such as 3-D model-based pose estimation, depth information can be exploited to achieve robust performance. We apply the proposed method for human motion capture (HMC), using the Human Eva I dataset. We compare our approach with alternative methods used for HMC. Our results show that using depth information makes traditional DCM robust. Furthermore, the proposed method outperforms the alternatives used for HMC in state of the art systems.