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Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection

Abstract · Apr 19, 2017 15:30 ·

multi people rithms occlusion crowded deep detection mcmt crf subtraction cs-cv

Arxiv Abstract

  • Pierre Baqué
  • François Fleuret
  • Pascal Fua

People detection in single 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 de- grades severely when scenes become very crowded. In this work, we introduce a new architecture that combines Con- volutional Neural Nets and Conditional Random Fields to explicitly model those ambiguities. One of its key ingredi- ents are high-order CRF terms that model potential occlu- sions 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-art algorithms on challenging scenes.

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