Shape from pairwise silhouettes for plan-view map generation ?
A plan-view map is a representation mechanism especially appropriated for people detection and tracking. It allows to represent volumetric information in a very compact and efficient way so that very effective detection and tracking algorithms can be applied using it. In spite of their advantages, plan-view maps have mainly been used with stereo cameras but not with monocular ones. The reason why is that many of the three-dimensional reconstruction algorithms using monocular vision impose strong conditions (such as controlled lightning, lack of occlusion, etc.) thus impeding their applicability in realistic environments. This paper presents two main novelties. First, a three-dimensional reconstruction algorithm that is especially appropriate for people detection and tracking because of its robustness to errors in the background subtraction. Second, a new technique for creating plan-view maps from volumetric reconstructions that reduces the amount of false positives in the occluded regions. We show experimentally that these techniques produce plan-view maps that can be reliably employed for tracking purposes. The proposed methods are compared with the traditional SfS obtaining relevant improvements.
plan-view maps shape from silhouettes person tracking particle filtering color processing condensation algorithm