Download PDFOpen PDF in browserSemantic Fusion-based Pedestrian Detection for Supporting Autonomous VehiclesEasyChair Preprint 37836 pages•Date: July 7, 2020AbstractTo increase traffic safety and transportation efficiency, adopting intelligent transportation systems (ITS) has become a trend. As an important component of ITS, one essential task of autonomous vehicles is to detect pedestrians accurately, which is of great significance for improving traffic safety and building a smart city. In this paper, we propose an anchor-free pedestrian detection model named Bi-Center Network (BCNet) by fusing the full body center and visible part center for each pedestrian. Experimental results show that the performance of pedestrian detection can be improved with a strengthened heatmap, which combines the full body with the visible part semantic. We compare our BCNet with state-of-the-art models on the CityPersons dataset and the ETH dataset, which shows that our approach is effective and achieves a promising performance. Keyphrases: Convolution Neural Network, Intelligent Transportation System, autonomous vehicle, pedestrian detection
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