  In the current worldwide situation, pedestrian detection has reemerged as a
pivotal tool for intelligent video-based systems aiming to solve tasks such as
pedestrian tracking, social distancing monitoring or pedestrian mass counting.
Pedestrian detection methods, even the top performing ones, are highly
sensitive to occlusions among pedestrians, which dramatically degrades their
performance in crowded scenarios. The generalization of multi-camera set-ups
permits to better confront occlusions by combining information from different
viewpoints. In this paper, we present a multi-camera approach to globally
combine pedestrian detections leveraging automatically extracted scene context.
Contrarily to the majority of the methods of the state-of-the-art, the proposed
approach is scene-agnostic, not requiring a tailored adaptation to the target
scenario\textemdash e.g., via fine-tunning. This noteworthy attribute does not
require \textit{ad hoc} training with labelled data, expediting the deployment
of the proposed method in real-world situations. Context information, obtained
via semantic segmentation, is used 1) to automatically generate a common Area
of Interest for the scene and all the cameras, avoiding the usual need of
manually defining it; and 2) to obtain detections for each camera by solving a
global optimization problem that maximizes coherence of detections both in each
2D image and in the 3D scene. This process yields tightly-fitted bounding boxes
that circumvent occlusions or miss-detections. Experimental results on five
publicly available datasets show that the proposed approach outperforms
state-of-the-art multi-camera pedestrian detectors, even some specifically
trained on the target scenario, signifying the versatility and robustness of
the proposed method without requiring ad-hoc annotations nor human-guided
configuration.
