K-tangent spaces on Riemannian manifolds for improved pedestrian detection

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Sanin, Andres
Sanderson, Conrad
Harandi, Mehrtash T
Lovell, Brian C
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2012
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Orlando, FL, USA
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Abstract

For covariance-based image descriptors, taking into account the curvature of the corresponding feature space has been shown to improve discrimination performance. This is often done through representing the descriptors as points on Riemannian manifolds, with the discrimination accomplished on a tangent space. However, such treatment is restrictive as distances between arbitrary points on the tangent space do not represent true geodesic distances, and hence do not represent the manifold structure accurately. In this paper we propose a general discriminative model based on the combination of several tangent spaces, in order to preserve more details of the structure. The model can be used as a weak learner in a boosting-based pedestrian detection framework. Experiments on the challenging INRIA and DaimlerChrysler datasets show that the proposed model leads to considerably higher performance than methods based on histograms of oriented gradients as well as previous Riemannian-based techniques.

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2012 19th IEEE International Conference on Image Processing
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© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Sanin, A; Sanderson, C; Harandi, MT; Lovell, BC, K-tangent spaces on Riemannian manifolds for improved pedestrian detection, 2012 19th IEEE International Conference on Image Processing, 2012, 473-476