Abstract
Pedestrian detection based on visual sensors has made significant progress, in which region proposal is the key step. There are two mainstream methods to generate region proposals: anchor-based and anchor-free. However, anchor-based methods need more hyperparameters related to anchors for training compared with anchor-free methods. In this paper, we propose a novel multiscale anchor-free (MSAF) region proposal network to obtain proposals, especially for small-scale pedestrians. It usually has several branches to predict proposals and assigns ground truth according to the height of pedestrian. Each branch consists of two components: one is feature extraction, and the other is detection head. Adapted channel feature fusion (ACFF) is proposed to select features at different levels of the backbone to effectively extract features. The detection head is used to predict the pedestrian center location, center offsets, and height to get bounding boxes. With our classifier, the detection performance can be further improved, especially for small-scale pedestrians. The experiments on the Caltech and CityPersons demonstrate that the MSAF can significantly boost the pedestrian detection performance and the log-average miss rate (MR) on the reasonable setting is 3.97% and 9.5%, respectively. If proposals are reclassified with our classifier, MR is 3.38% and 8.4%. The detection performance can be further improved, especially for small-scale pedestrians.
Highlights
Pedestrian detection played an important role in self-driving vehicle tasks by assisting drivers to judge whether there are pedestrians in the front of the driving area
We focus on analyzing the impact of the multiscale anchor-free (MSAF) and Adapted channel feature fusion (ACFF)
To improve the pedestrian detection performance, a multiscale anchor-free region proposal network is proposed in this paper
Summary
Pedestrian detection played an important role in self-driving vehicle tasks by assisting drivers to judge whether there are pedestrians in the front of the driving area. Pedestrian detection performance directly affects pedestrian safety [1,2,3,4]. With the research and development of convolutional neural networks (CNN), pedestrian detection methods based on CNN have shown rapid progress. According to the regression starting status, pedestrian detection can be divided into anchor-based [5,6,7,8,9] and anchor-free [10,11,12,13,14,15,16] detection methods. Based on the number of the detection stages, anchorbased methods can be divided into two-stage [9, 17,18,19,20] and one-stage [6, 21, 22] detection methods. In one-stage detection, the final detection results can be obtained via only one step; pedestrian detection can skip the classification stage and predict bounding boxes with confidence scores directly
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
