Nowadays, pedestrian detection is one of the pivotal fields in computer vision, especially when performed over video surveillance scenarios. People detection methods are highly sensitive to occlusions among pedestrians, which dramatically degrades performance in crowded scenarios. The cutback in camera prices has allowed generalizing multi-camera set-ups, which can better confront occlusions by using different points of view to disambiguate detections. In this paper we present an approach to improve the performance of these multi-camera systems and to make them independent of the considered scenario, via an automatic understanding of the scene content. This semantic information, obtained from a semantic segmentation, is used 1) to automatically generate a common Area of Interest for all cameras, instead of the usual manual definition of this area; and 2) to improve the 2D detections of each camera via an optimization technique which maximizes coherence of every detection both in all 2D views and in the 3D world, obtaining best-fitted bounding boxes and a consensus height for every pedestrian. Experimental results on five publicly available datasets show that the proposed approach, which does not require any training stage, outperforms state-of-the-art multi-camera pedestrian detectors non specifically trained for these datasets, which demonstrates the expected semantic-based robustness to different scenarios.
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. This paper addresses the problem of multi-view people occupancy map estimation. Existing solutions for this problem either operate per-view, or rely on a background subtraction pre-processing. Both approaches lessen the detection performance as scenes become more crowded. The former does not exploit joint information, whereas the latter deals with ambiguous input due to the foreground blobs becoming more and more interconnected as the number of targets increases. Although deep learning algorithms have proven to excel on remarkably numerous computer vision tasks, such a method has not been applied yet to this problem. In large part this is due to the lack of large-scale multi-camera data-set. The core of our method is an architecture which makes use of monocular pedestrian data-set, available at larger scale then the multi-view ones, applies parallel processing to the multiple video streams, and jointly utilises it. Our end-to-end deep learning method outperforms existing methods by large margins on the commonly used PETS 2009 data-set. Furthermore, we make publicly available a new three-camera HD data-set. Our source code and trained models will be made available under an open-source license.