An Improved D-CNN Based on YOLOv3 for Pedestrian Detection
Faizan Ahmad, Ning Li, Mustafa Tahir · 2019
Recent developments in pedestrian detection techniques shows that former algorithms cannot satisfy accurate and speedy detection for practical applications. Adding to the list of modern algorithms in deep learning, these methods are capable to fulfill the requirement of modern applications for pedestrian detection. In this paper, a deep convolutional neural network (D-CNN) based single class You Only Look Once (YOLOv3) state-of-the-art approach is proposed to overcome the problem of pedestrian detection in the contemporary application namely advanced driving assistance system (ADAS), and video surveillance system in terms of false detection (FD) and miss rate (MR). The proposed model is trained on INRIA datasets, which are universally applicable for pedestrian detection. Furthermore, it is effectively demonstrated in different scenarios.