Directional eigentemplate learning for sparse template tracker
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
Real-time face tracking and recognition by sparse eigentracker with associative mapping to 3D shape
Image and Vision Computing
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Tracking 3d pose of a known object is one of the most important problems in computer vision. This paper proposes an appearance-based approach to this problem by combining the sparse template matching and the particle filter. Although the combination of them has already been discussed for 2d tracker, it has not been applied for efficient 3d tracking. This paper discusses an appearance-based tracker when a surface model of the target 3d object and the initial pose are given. The fundamental framework of the particle filter is provided at first for implementing a pose tracker based on sparse 3d template matching. Then, the coarse-to-fine approach is introduced for efficient implementation. Although the fundamental particle filter often requires a lot of particles for sufficient tracking, the number of particles can be effectively reduced by the coarse-to-fine strategy. Experimental results for both the simulation data and real images show how the proposed method works in frame rate on 3GHz Core 2 Quad (single thread) without using GPU or other special hardware.