Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection

Pierre Baque; Francois Fleuret; Pascal Fua

ICCV 2017poster127 citations

Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection

Pierre Baque, Francois Fleuret, Pascal Fua

Abstract

People detection in 2D images has improved greatly in recent years. However, comparatively little of this progress has percolated into multi-camera multi-people tracking algorithms, whose performance still degrades severely when scenes become very crowded. In this work, we introduce a new architecture that combines Convolutional Neural Nets and Conditional Random Fields to explicitly resolve ambiguities. One of its key ingredients are high-order CRF terms that model potential occlusions and give our approach its robustness even when many people are present. Our model is trained end-to-end and we show that it outperforms several state-of-the-art algorithms on challenging scenes.

BibTeX

@inproceedings{iccv2017_deepocclusionrea,
  title = {Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection},
  author = {Pierre Baque and Francois Fleuret and Pascal Fua},
  booktitle = {ICCV 2017},
  year = {2017}
}