1 research outputs found
Superpixel Tensor Pooling for Visual Tracking using Multiple Midlevel Visual Cues Fusion
In this paper, we propose a method called superpixel tensor pooling tracker
which can fuse multiple midlevel cues captured by superpixels into sparse
pooled tensor features. Our method first adopts the superpixel method to
generate different patches (superpixels) from the target template or
candidates. Then for each superpixel, it encodes different midlevel cues
including HSI color, RGB color, and spatial coordinates into a histogram matrix
to construct a new feature space. Next, these matrices are formed to a third
order tensor. After that, the tensor is pooled into the sparse representation.
Then the incremental positive and negative subspaces learning is performed. Our
method has both good characteristics of midlevel cues and sparse representation
hence is more robust to large appearance variations and can capture compact and
informative appearance of the target object. To validate the proposed method,
we compare it with state-of-the-art methods on 24 sequences with multiple
visual tracking challenges. Experiment results demonstrate that our method
outperforms them significantly.Comment: 8 pages, 7 figures. in IEEE Acces