  Improving multi-view aggregation is integral for multi-view pedestrian
detection, which aims to obtain a bird's-eye-view pedestrian occupancy map from
images captured through a set of calibrated cameras. Inspired by the success of
attention modules for deep neural networks, we first propose a Homography
Attention Module (HAM) which is shown to boost the performance of existing
end-to-end multiview detection approaches by utilizing a novel channel gate and
spatial gate. Additionally, we propose Booster-SHOT, an end-to-end
convolutional approach to multiview pedestrian detection incorporating our
proposed HAM as well as elements from previous approaches such as view-coherent
augmentation or stacked homography transformations. Booster-SHOT achieves 92.9%
and 94.2% for MODA on Wildtrack and MultiviewX respectively, outperforming the
state-of-the-art by 1.4% on Wildtrack and 0.5% on MultiviewX, achieving
state-of-the-art performance overall for standard evaluation metrics used in
multi-view pedestrian detection.
