Multi-object detection and tracking by stereo vision
Pattern Recognition
3D motion segmentation using intensity trajectory
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Heritage pieces integration in autonomous augmented reality systems: key problems and solutions
VAST'08 Proceedings of the 9th International conference on Virtual Reality, Archaeology and Cultural Heritage
Bayesian shape from silhouettes
MUSCLE'11 Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding
Bayesian 3D shape from silhouettes
Digital Signal Processing
| Hi-index | 0.00 |
We propose a multiview tracking method for rigid objects. Assuming that a part of the object is visible in at least two cameras, a partial 3D model is reconstructed in terms of a collection of small 3D planar patches of arbitrary topology. The 3D representation, recovered fully automatically, allows to formulate tracking as gradient minimization in pose (translation, rotation) space. As the object moves, the 3D model is incrementally updated. A virtuous circle emerges: tracking enables composition of the partial 3D model; the 3D model facilitates and robustifies the multi-view tracking. We demonstrate experimentally that the interleaved track-and-reconstruct approach successfully tracks a 360 degrees turn-around and a wide range of motions. Monocular tracking is also possible after the model is constructed. Using more cameras, however, significantly increases stability in critical poses and moves. We demonstrate how to exploit the 3D model to increases stability in the presence of uneven and/or changing illumination.