Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-Free Approach | |
Lingxiao He1,2; Jian Liang1,2; Haiqing Li1,2; Zhenan Sun1,2,3 | |
2018 | |
会议名称 | IEEE Conference on Computer Vision and Pattern Recognition (CVPR) |
会议日期 | 2018 |
会议地点 | USA |
摘要 | Partial person re-identification (re-id) is a challenging problem, where only some partial observations (images) of persons are available for matching. However, few studies have offered a flexible solution of how to identify an arbitrary patch of a person image. In this paper, we propose a fast and accurate matching method to address this problem. The proposed method leverages Fully Convolutional Network (FCN) to generate certain-sized spatial feature maps such that pixel-level features are consistent. To match a pair of person images of different sizes, hence, a novel method called Deep Spatial feature Reconstruction (DSR) is further developed to avoid explicit alignment. Specifically, DSR exploits the reconstructing error from popular dictionary learning models to calculate the similarity between different spatial feature maps. In that way, we expect that the proposed FCN can decrease the similarity of coupled images from different persons and increase that of coupled images from the same person. Experimental results on two partial person datasets demonstrate the efficiency and effectiveness of the proposed method in comparison with several stateof-the-art partial person re-id approaches. Additionally, it achieves competitive results on a benchmark person dataset Market1501 with the Rank-1 accuracy being 83.58%. |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/23806 |
专题 | 智能感知与计算研究中心 |
作者单位 | 1.CRIPAC \& NLPR, Institute of Automation, Chinese Academy of Sciences (CAS) 2.Center for Excellence in Brain Science and Intelligence Technology, CAS 3.University of Chinese Academy of Sciences, Beijing, P.R. China |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Lingxiao He,Jian Liang,Haiqing Li,et al. Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-Free Approach[C],2018. |
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