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arXiv · 1704.05775

Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection

Pierre Baqué·François Fleuret·Pascal Fua
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Abstract

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 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 model those 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-art algorithms on challenging scenes.

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Pierre Baqué, François Fleuret, Pascal Fua. 2017-04-19. Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection. https://arxiv.org/abs/1704.05775

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