Name
Abstract | ||
|---|---|---|
To 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. Compared to the backbone model, our BCNet improves the detection accuracy by 1.2% on the Reasonable setup and Partial Setup of the CityPersons dataset. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ISCC50000.2020.9219723 | 2020 IEEE Symposium on Computers and Communications (ISCC) |
Keywords | DocType | ISSN |
Intelligent transportation system,autonomous vehicle,pedestrian detection,convolutional neural network | Conference | 1530-1346 |
ISBN | Citations | PageRank |
978-1-7281-8086-1 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingzhi Sha | 1 | 0 | 1.69 |
| Azzedine Boukerche | 2 | 40 | 19.46 |