Faster R-CNN with densenet for scale aware pedestrian detection vis-à-vis hard negative suppression

Abstract

This paper presents a fully convolutional architecture for pedestrian detection. The DenseNet model is incorporated in the Faster R-CNN framework to extract the deep convolutional features. A two-phase approach is suggested to minimize the false positives owing to hard negative backgrounds. Feature maps from multiple intermediate layers are taken into consideration to facilitate small-scale detection. The proposed method alongside few competent schemes are compared on two benchmark datasets. The obtained results demonstrate the potential of our approach in addressing the real world challenges.