Multiview pedestrian localisation via a prime candidate chart based on occupancy likelihoods
Yuyao Yan, Ming Xu, Jeremy S. Smith · 2017
A sound way to localize occluded people is to project the foregrounds from multiple camera views to a reference view by homographies and find the foreground intersections. However, this may give rise to phantoms due to foreground intersections from different people. In this paper, each intersection region is warped back to the original camera view and is associated with a candidate box of the average size of pedestrians at that location. Then a joint occupancy likelihood is calculated for each intersection region. In the second step, essential candidate boxes are identified first, each of which covers at least a part of the foreground that is not covered by another candidate box. The non-essential candidate boxes are selected to cover the remaining foregrounds in the order of their joint occupancy likelihoods. Experiments on benchmark video datasets have demonstrated the good performance of our algorithm in comparison with other state-of-the-art methods.