Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
A Novel Data Augmentation Scheme for Pedestrian Detection with Attribute Preserving GAN | |
Liu, Songyan1,2; Guo, Haiyun1,2; Hu, Jian-Guo3; Zhao, Xu1,2; Zhao, Chaoyang1,2; Wang, Tong1,2; Zhu, Yousong1,2; Wang, Jinqiao1,2; Tang, Ming1,2 | |
发表期刊 | Neurocomputing |
ISSN | 0925-2312 |
2020-08-11 | |
卷号 | 401期号:11页码:123-132 |
摘要 | Recently pedestrian detection has progressed significantly. However, detecting pedestrians of small scale or in heavy occlusions is still notoriously difficult. Besides, the generalization ability of pre-trained detectors across different datasets remains to be improved. Both of these issues can be attributed to insufficient training data coverage. To cope with this, we present an efficient data augmentation scheme by transferring pedestrians from other datasets into the target scene with a novel Attribute Preserving Generative Adversarial Networks (APGAN). The proposed methodology consists of two steps: pedestrian embedding and style transfer. The former step can simulate pedestrian images of various scale and occlusion, in any pose or background, thus greatly promoting the data variation. The latter step aims to make the generated samples more realistic while guarantee the data coverage. To achieve this goal, we propose APGAN, which pursues both good visual quality and attribute preserving after style transfer. With the proposed method, we can make effective sample augmentations to improve the generalization ability of the trained detectors and enhance its robustness to scale change and occlusions. Extensive experiment results validate the effectiveness and advantages of our method. |
关键词 | Generative Adversarial Networks Pedestrian detection Data augmentation |
DOI | 10.1016/j.neucom.2020.02.094 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61806200] ; National Natural Science Foundation of China[61772527] ; National Natural Science Foundation of China[61772527] ; National Natural Science Foundation of China[61806200] |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000544725700012 |
出版者 | ELSEVIER |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39134 |
专题 | 模式识别国家重点实验室_图像与视频分析 |
通讯作者 | Guo, Haiyun; Hu, Jian-Guo |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Sun Yat-Sen University, Guangzhou 510000, China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Liu, Songyan,Guo, Haiyun,Hu, Jian-Guo,et al. A Novel Data Augmentation Scheme for Pedestrian Detection with Attribute Preserving GAN[J]. Neurocomputing,2020,401(11):123-132. |
APA | Liu, Songyan.,Guo, Haiyun.,Hu, Jian-Guo.,Zhao, Xu.,Zhao, Chaoyang.,...&Tang, Ming.(2020).A Novel Data Augmentation Scheme for Pedestrian Detection with Attribute Preserving GAN.Neurocomputing,401(11),123-132. |
MLA | Liu, Songyan,et al."A Novel Data Augmentation Scheme for Pedestrian Detection with Attribute Preserving GAN".Neurocomputing 401.11(2020):123-132. |
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