Multiple view, multiple target tracking with principal axis-based data association
Francisco Javier Alonso Madrigal, Jean-Bernard Hayet · 2011
We present a novel method for multi-object tracking that tracks target both in the video streams and in a reference ground frame. This allows to remove ambiguities created by occlusions in one view. Our system takes as a base a recently proposed collaborative scheme and makes it handle multiple targets. We use a fast, simple solution for data association in the ground plane based on principal axis and a partly joint probabilistic model with MCMC sampling to ensure that tracked targets are kept separated whenever groups of targets appear. Results are presented on several popular databases of multi-camera, multi-target videos.