关键词: 均值漂移, 尺度空间, 稀疏编码, 空间金字塔, 部分遮挡

Abstract: This paper proposes a robust method for visual tracking relying on Meanshift, sparse coding and spatial pyramids. Firstly, it extends the original Meanshift approach to handle orientation space and scale space and names this new method as mean transform. The mean transform method estimates the motion, including the location, orientation and scale, of the interested object window simultaneously and effectively. Secondly, a pixel-wise dense patch sampling technique and a region-wise trivial template designing scheme are introduced which enable this approach to run very accurately and efficiently. In addition, instead of using either holistic representation or local representation only, it applies spatial pyramids by combining these two representations into this approach to deal with partial occlusion problems robustly. Observed from the experimental results, this approach outperforms state-of-the-art methods in many benchmark sequences.

Key words: Meanshift, scale space, sparse coding, spatial pyramids, partial occlusion