以可變部件模型為基礎之遠/近紅外線特徵融合夜間行人偵測

Abstract

行人偵測為駕駛安全輔助系統中相當重要的一環，但目前此領域之研究方向大多集中在日間影像上，而忽略了較易發生意外的夜間。此外，許多夜間行人偵測研究者也遭遇到無法取得有效資料庫之問題。而夜間影像的取得可使用遠紅外線與近紅外線兩種攝影設備，兩者因具有彼此互補之優缺點，相當適合進行影像融合增進行人偵測系統之準確率。而在物體偵測器方面，可變部件模型為目前已知最成功、最被廣泛應用與研究的單一日間影像物體偵測器。綜上所述，本文以遠紅外線及近紅外線攝影機取得夜間影像、建立資料庫，並以可變部件模型建立一遠/近紅外線特徵融合夜間行人偵測系統。實驗結果顯示，本系統之偵測率相較於單一遠/近紅外線影像有顯著的提升，也優於其它相關研究之影像融合行人偵測系統。

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.