Learning Coarse-to-fine Structured Feature Embedding for Vehicle Re-identification
Guo Haiyun1,2; Zhao Chaoyang1,2; Liu Zhiwei1,2; Wang Jinqiao1,2; Lu Hanqing1,2
2018
会议名称AAAI Conference on Artificial Intelligence
会议日期2018年2月2号-7号
会议地点New Orleans, Louisiana, USA
摘要Vehicle re-identification (re-ID) is to identify the same vehicle across different cameras. It’s a significant but challenging topic, which has received little attention due to the complex intra-class and inter-class variation of vehicle images and the lack of large-scale vehicle re-ID dataset. Previous methods focus on pulling images from different vehicles apart but neglect the discrimination between vehicles from different vehicle models, which is actually quite important to obtain a correct ranking order for vehicle re-ID. In this paper, we learn a structured feature embedding for vehicle re-ID with a novel coarse-to-fine ranking loss to pull images of the same vehicle as close as possible and achieve discrimination between images from different vehicles as well as vehicles from different vehicle models. In the learnt feature space, both intra-class compactness and inter-class distinction are well guaranteed and the Euclidean distance between features directly reflects the semantic similarity of vehicle images. Furthermore, we build so far the largest vehicle re-ID dataset “Vehicle-1M”1 which involves nearly 1 million images captured in various surveillance scenarios. Experimental results on “Vehicle-1M”and “VehicleID” demonstrate the superiority of our proposed approach.
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/20902
专题模式识别国家重点实验室_图像与视频分析
通讯作者Zhao Chaoyang
作者单位1.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714
Guo Haiyun,Zhao Chaoyang,Liu Zhiwei,et al. Learning Coarse-to-fine Structured Feature Embedding for Vehicle Re-identification[C],2018.
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