A novel visibility semantic feature-aided pedestrian detection scheme for autonomous vehicles

Intelligent transportation systems (ITS) have become a popular method for enhancing transportation safety and efficiency. As essential participants of ITS, autonomous vehicles need to detect pedestrians accurately. In this paper, we propose a one-stage anchor-free pedestrian detection model named Bi-Center Network (BCNet), which is aided by the semantic features of pedestrians' visible parts. We perform an ablation study to discover how visibility features could benefit the detector's performance, including introducing two hyper-parameters and adopting three different attention mechanisms, respectively. The experimental results indicate that the performance of pedestrian detection could be significantly improved, since the visibility semantic could prompt stronger responses on the heatmap. We compare our BCNet variants with state-of-the-art models on the CityPersons dataset and ETH dataset; results indicate that our detector is effective and achieves a promising performance.

Automated summary

This study introduces a new pedestrian detection model BCNet, which utilizes semantic features of pedestrians' visible parts to enhance detection performance, and experiments show that the model significantly improves accuracy. Through ablation study and comparison experiments, it is demonstrated that BCNet achieves good performance on CityPersons dataset and ETH dataset.