상세정보
학술지
Pedestrian Detection with Minimal False Positives per Color-Thermal Image
- 발행일
- 201702
- 출처
- The Arabian Journal for Science and Engineering, v.42 no.8, pp.3207-3219
- ISSN
- 2193-567X
- 출판사
- King Fahd University of Petroleum & Minerals
- 협약과제
- 17ZD1200, 상황인지 스마트카 퓨전 플랫폼 개발 및 지역 부품업체 지원사업, 박미룡
- 초록
- This research is based on aggregate channel features utilized for pedestrian detection, and the main focus is to investigate a simple way to reduce the number of false positives per image. The importance of this will be to increase the accuracy of the detector by removing the excessive number of false positives while maintaining the missing rate as low as possible. To omit such unwanted false positives, we utilized an image categorization method for day and night images in order to minimize the misclassification rate. Furthermore, the best extension of the aggregate channel features method was analyzed and is recommended as a base detector. As a result, a night-time pre-trained pedestrian detector is only applied to night images, and a daytime detector is applied to daytime images. Thus, a large number of false positives are avoided while the missing rate is greatly reduced.
- 키워드
- Aggregated channel features, Computer vision, False positive removal, KAIST visible-thermal image dataset, Pedestrian detectors
- KSP 제안 키워드
- Aggregate channel features, Aggregated Channel Features(ACF), Computer Vision(CV), False Positive Removal, Image datasets, Misclassification rate, Missing rate, Night images, Night-time, Pedestrian detectors, Thermal image