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Improving multi-view aggregation is integral for multi-view pedestriandetection, which aims to obtain a bird's-eye-view pedestrian occupancy map fromimages captured through a set of calibrated cameras. Inspired by the success ofattention modules for deep neural networks, we first propose a HomographyAttention Module (HAM) which is shown to boost the performance of existingend-to-end multiview detection approaches by utilizing a novel channel gate andspatial gate. Additionally, we propose Booster-SHOT, an end-to-endconvolutional approach to multiview pedestrian detection incorporating ourproposed HAM as well as elements from previous approaches such as view-coherentaugmentation or stacked homography transformations. Booster-SHOT achieves 92.9%and 94.2% for MODA on Wildtrack and MultiviewX respectively, outperforming thestate-of-the-art by 1.4% on Wildtrack and 0.5% on MultiviewX, achievingstate-of-the-art performance overall for standard evaluation metrics used inmulti-view pedestrian detection.

Booster-SHOT: Boosting Stacked Homography Transformations for Multiview Pedestrian Detection with Attention