Multispectral pedestrian detection is essential for around-the-clock
applications, e.g., surveillance and autonomous driving. We deeply analyze
Faster R-CNN for multispectral pedestrian detection task and then model it into
a convolutional network (ConvNet) fusion problem. Further, we discover that
ConvNet-based pedestrian detectors trained by color or thermal images
separately provide complementary information in discriminating human instances.
Thus there is a large potential to improve pedestrian detection by using color
and thermal images in DNNs simultaneously. We carefully design four ConvNet
fusion architectures that integrate two-branch ConvNets on different DNNs
stages, all of which yield better performance compared with the baseline
detector. Our experimental results on KAIST pedestrian benchmark show that the
Halfway Fusion model that performs fusion on the middle-level convolutional
features outperforms the baseline method by 11% and yields a missing rate 3.5%
lower than the other proposed architectures.

本论文通过对 Faster R-CNN 进行多光谱行人检测任务的深入分析，并将其建模为卷积神经网络（ConvNet）融合问题，同时发现使用彩色和热成像图像训练的基于 ConvNet 的行人检测器在区分人类实例方面提供了互补信息，其在 KAIST 行人基准上的实验结果表明，在不同 DNN 阶段集成两个分支 ConvNets 的四种 ConvNet 融合结构中，半路融合模型在中间级卷积特征上的结合优于基线方法 11％，遗漏率比其他建议的架构低 3.5％。