Deep Multi-Camera People Detection Tatjana Chavdarova and François Fleuret Idiap Research Institute and École Polytechnique Fédérale de Lausanne Email: {firstname.lastname}@idiap.ch Abstract—This paper addresses the problem of multi-view peo- ple 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 d
