An Efficient Pedestrian Detection Method Based on YOLOv2
Joint Authors
Liu, Zhongmin
Chen, Zhicai
Li, Zhanming
Hu, Wenjin
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-31
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
In recent years, techniques based on the deep detection model have achieved overwhelming improvements in the accuracy of detection, which makes them being the most adapted for the applications, such as pedestrian detection.
However, speed and accuracy are a pair of contradictions that always exist and have long puzzled researchers.
How to achieve the good trade-off between them is a problem we must consider while designing the detectors.
To this end, we employ the general detector YOLOv2, a state-of-the-art method in the general detection tasks, in the pedestrian detection.
Then we modify the network parameters and structures, according to the characteristics of the pedestrians, making this method more suitable for detecting pedestrians.
Experimental results in INRIA pedestrian detection dataset show that it has a fairly high detection speed with a small precision gap compared with the state-of-the-art pedestrian detection methods.
Furthermore, we add weak semantic segmentation networks after shared convolution layers to illuminate pedestrians and employ a scale-aware structure in our model according to the characteristics of the wide size range in Caltech pedestrian detection dataset, which make great progress under the original improvement.
American Psychological Association (APA)
Liu, Zhongmin& Chen, Zhicai& Li, Zhanming& Hu, Wenjin. 2018. An Efficient Pedestrian Detection Method Based on YOLOv2. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1206876
Modern Language Association (MLA)
Liu, Zhongmin…[et al.]. An Efficient Pedestrian Detection Method Based on YOLOv2. Mathematical Problems in Engineering No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1206876
American Medical Association (AMA)
Liu, Zhongmin& Chen, Zhicai& Li, Zhanming& Hu, Wenjin. An Efficient Pedestrian Detection Method Based on YOLOv2. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1206876
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1206876