K-Tangent Spaces on Riemannian Manifolds for Improved Pedestrian Detection
This work addresses pedestrian detection for computer vision applications, presenting an incremental improvement over existing Riemannian-based techniques.
The paper tackles the problem of inaccurate geodesic distance representation in Riemannian manifold-based pedestrian detection by proposing a discriminative model that combines multiple tangent spaces to better preserve manifold structure. Experiments on INRIA and DaimlerChrysler datasets show it outperforms histogram of oriented gradients and previous Riemannian methods, though specific performance numbers are not provided.
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.