Multispectral Deep Neural Networks for Pedestrian Detection
This work addresses pedestrian detection for around-the-clock applications like surveillance and autonomous driving, but it is incremental as it builds on existing Faster R-CNN methods.
The paper tackled pedestrian detection using multispectral (color and thermal) images by designing four convolutional network fusion architectures, with the Halfway Fusion model improving performance by 11% over the baseline and reducing the missing rate by 3.5% compared to other architectures.
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.