Name
Title | ||
|---|---|---|
A Novel Internet-Of-Vehicles Assisted Collaborative Low-Visible Pedestrian Detection Approach |
Abstract | ||
|---|---|---|
For releasing the public concern on road safety, as an essential driving assistant technique for supporting autonomous deriving, considerable research efforts have been paid on developing practical traffic-related targel/object detection methods. In recent years, by exploiting the powerful parallel processing capability of GPU and the feature extraction ability of deep convolutional neural network (CNN), the visible light image-based pedestrian detection method has gradually been considered as a potential solution. However, although it has been proven in the existing literature that CNN-based pedestrian detection methods can greatly improve the detection efficiency for lightly occluded pedestrians, the detection of low-visible pedestrians is still an open challenge. Accordingly, in this paper, we propose a novel collaborative pedestrian detection frame based on the Internet-of-Vehicles (boy) to detect low-visible/hidden pedestrians or even hidden pedestrians. We further evaluate the proposed pedestrian detection framework relying on simulation experiments. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/GLOBECOM42002.2020.9322113 | 2020 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) |
Keywords | DocType | ISSN |
Autonomous driving, Internet-of-Vehicles, artificial intelligence, traffic condition sensing, low-visible pedestrian detection | Conference | 2334-0983 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peng Sun | 1 | 150 | 27.89 |
| Boukerche, A. | 2 | 61 | 16.98 |