Recognizing pedestrian's unsafe behaviors in far-infrared imagery at night
- Title
- Recognizing pedestrian's unsafe behaviors in far-infrared imagery at night
- Author
- 이은주
- Keywords
- Unsafe behavior recognition; Advance driver assistance system; Convolution neural network; Genetic algorithm; Spatial pyramid pooling; Boosted random forest
- Issue Date
- 2016-05
- Publisher
- ELSEVIER SCIENCE BV
- Citation
- INFRARED PHYSICS & TECHNOLOGY, v. 76, Page. 261-270
- Abstract
- Pedestrian behavior recognition is important work for early accident prevention in advanced driver assistance system (ADAS). In particular, because most pedestrian-vehicle crashes are occurred from late of night to early of dawn, our study focus on recognizing unsafe behavior of pedestrians using thermal image captured from moving vehicle at night. For recognizing unsafe behavior, this study uses convolutional neural network (CNN) which shows high quality of recognition performance. However, because traditional CNN requires the very expensive training time and memory, we design the light CNN consisted of two convolutional layers and two subsampling layers for real-time processing of vehicle applications. In addition, we combine light CNN with boosted random forest (Boosted RF) classifier so that the output of CNN is not fully connected with the classifier but randomly connected with Boosted random forest. We named this CNN as randomly connected CNN (RC-CNN). The proposed method was successfully applied to the pedestrian unsafe behavior (PUB) dataset captured from far-infrared camera at night and its behavior recognition accuracy is confirmed to be higher than that of some algorithms related to CNNs, with a shorter processing time. (C) 2016 Elsevier B.V. All rights reserved.
- URI
- https://www.sciencedirect.com/science/article/pii/S1350449516300652https://repository.hanyang.ac.kr/handle/20.500.11754/100541
- ISSN
- 1350-4495; 1879-0275
- DOI
- 10.1016/j.infrared.2016.03.006
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ETC
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML