Nighttime Pedestrian Detection with Far/Near Infrared Feature Level Fusion Based on a Deformable Part Model
| Autor: | Zhe-Yi Lin, 林哲逸 |
|---|---|
| Rok vydání: | 2012 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 101 Pedestrian detection is a crucial part in driver assistance system, but reseach direction of the field at present is mainly on day light images, while little is on nighttime, when accidents happen more. Moreover, nighttime pedestrian database is hard to access from public for the researchers in the field of nighttime pedestrian Detection, which leads to a large amount of time consuming for database setup. Far infrared and near infrared are two choices for night vision, which are nicely complemented to each other, and therefore suitable for image fusion to improve detection rate of pedestrian detection system. For object detector, deformable part model is the most successful and well researched detector among all presently. Accordingly, this article built a public nighttime pedestrian database from far and near infrared camera, and built a far/near infrared feature level fusion nighttime pedestrian detection system, which is based on deformable part model. Experimental result shows that our system’s detection rate has a significant improvement to single sensor system, and is better than other nighttime pedestrian systems in this field. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
| Externí odkaz: |
|