  The variety of pedestrians detectors proposed in recent years has encouraged
some works to fuse pedestrian detectors to achieve a more accurate detection.
The intuition behind is to combine the detectors based on its spatial
consensus. We propose a novel method called Content-Based Spatial Consensus
(CSBC), which, in addition to relying on spatial consensus, considers the
content of the detection windows to learn a weighted-fusion of pedestrian
detectors. The result is a reduction in false alarms and an enhancement in the
detection. In this work, we also demonstrate that there is small influence of
the feature used to learn the contents of the windows of each detector, which
enables our method to be efficient even employing simple features. The CSBC
overcomes state-of-the-art fusion methods in the ETH dataset and in the Caltech
dataset. Particularly, our method is more efficient since fewer detectors are
necessary to achieve expressive results.
