Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fisher Motion Descriptor for Multiview Gait Recognition   The goal of this paper is to identify individuals by analyzing their gait.
Instead of using binary silhouettes as input data (as done in many previous
works) we propose and evaluate the use of motion descriptors based on densely
sampled short-term trajectories. We take advantage of state-of-the-art people
detectors to define custom spatial configurations of the descriptors around the
target person, obtaining a rich representation of the gait motion. The local
motion features (described by the Divergence-Curl-Shear descriptor) extracted
on the different spatial areas of the person are combined into a single
high-level gait descriptor by using the Fisher Vector encoding. The proposed
approach, coined Pyramidal Fisher Motion, is experimentally validated on
`CASIA' dataset (parts B and C), `TUM GAID' dataset, `CMU MoBo' dataset and the
recent `AVA Multiview Gait' dataset. The results show that this new approach
achieves state-of-the-art results in the problem of gait recognition, allowing
to recognize walking people from diverse viewpoints on single and multiple
camera setups, wearing different clothes, carrying bags, walking at diverse
speeds and not limited to straight walking paths.

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The categories of this paper are: math
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The categories of this paper are: adap-org
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The categories of this paper are: chao-dyn
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The categories of this paper are: cmp-lg
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The categories of this paper are: nlin
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The categories of this paper are: hep-lat
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The categories of this paper are: q-alg
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The categories of this paper are: astro-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: cond-mat
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The categories of this paper are: supr-con
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The categories of this paper are: quant-ph
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The categories of this paper are: math-ph
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-th
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The categories of this paper are: gr-qc