论文标题
助推器射击：提高堆叠的堆积量转换，以供多维图行人检测引起注意
Booster-SHOT: Boosting Stacked Homography Transformations for Multiview Pedestrian Detection with Attention
论文作者
论文摘要
改进的多视图聚合是多视图行人检测不可或缺的一部分，该检测旨在从通过一组校准的摄像机捕获的图像中获取鸟类视图的行人占用图。受到深度神经网络的关注模块成功的启发，我们首先提出了一个同构型注意模块（HAM），该模块通过使用新颖的通道门和空间门来提高现有端到端多视图检测方法的性能。此外，我们提出了Booster-shot，这是一种端到端的卷积方法，用于纳入我们所提出的HAM以及先前方法的元素，例如视图增强或堆叠的别于静态变换。在Wildtrack和Multiviewx上，Moda的Booster-Shot在Wildtrack上的Moda和ModiviewX上的Moda分别效果分别为94.2％，在Wildtrack上的表现优于1.4％，在Multiviewx上，胜过0.5％，实现了最先进的性能，以实现在多视图行人探测中使用的标准评估指标。
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.
