跨模态行人重识别研究与展望
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国家自然科学基金(61471182);江苏省研究生创新工程(CXLX13_70,KYCX17_1845)


Research and Prospect of Cross Modality Person Re-Identification
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    摘要:

    行人重识别是计算机视觉的热门研究方向,其对智能安防、视频监控的发展有着重大意义.目前大部分工作主要集中在研究基于可见光的行人重识别,然而可见光摄像头无法在光线不足的黑夜中正常使用,而新型摄像头能够随机切换红外模式进行24小时视频监控,因此最近有一些工作对RGB-IR跨模态行人重识别问题进行了研究.本文分别从定义、研究难点和发展现状介绍了跨模态行人重识别问题,并根据不同的技术类型将目前存在的方法分为三类,即基于统一特征模型的方法;基于度量学习的方法;基于模态转换的方法.本文也详细介绍了该任务的数据集和评价准则,并对现有算法的性能进行分析与归纳.最后,总结了跨模态行人重识别的未来发展方向.

    Abstract:

    Person re-identification (Re-ID) has attract lots of attention in computer vision, which is of great significance to the development of intelligent security and video surveillance. Currently, most existing methods focus on the person re-identification based on visible light, and have achieved good performance. However, the visible light camera cannot be used normally in the dark night, and the new generation of cameras can automatically switch the mode between infrared and visible settings for 24 hours monitoring. Therefore, some scholars have started to study the RGB-IR cross-modality pedestrian re-identification. This paper introduces the Re-ID and cross-modality Re-ID respectively from the definition, research difficulties, and development status. For RGB-IR cross-modality Re-ID, according to the types of methods, they are divided into three categories: methods based on unified feature models; methods based on metric learning; and methods based on modal transformation. We also describe the corresponding datasets and evaluation protocol. Besides, we analyze and summarize the performance of existing algorithms. Finally, the future development directions of RGB-IR cross-modality Re-ID are summarized.

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陈丹,李永忠,于沛泽,邵长斌.跨模态行人重识别研究与展望.计算机系统应用,2020,29(10):20-28

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  • 收稿日期:2020-03-14
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  • 在线发布日期: 2020-09-30
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