Abstract
Person re-identification (ReID) plays a crucial role in video surveillance with the aim to search a specific person across disjoint cameras, and it has progressed notably in recent years. However, visible cameras may not be able to record enough information about the pedestrian’s appearance under the condition of low illumination. On the contrary, thermal infrared images can significantly mitigate this issue. To this end, combining visible images with infrared images is a natural trend, and are considerably heterogeneous modalities. Some attempts have recently been contributed to visible-infrared person re-identification (VI-ReID). This paper provides a complete overview of current VI-ReID approaches that employ deep learning algorithms. To align with the practical application scenarios, we first propose a new testing setting and systematically evaluate state-of-the-art methods based on our new setting. Then, we compare ReID with VI-ReID in three aspects, including data composition, challenges, and performance. According to the summary of previous work, we classify the existing methods into two categories. Additionally, we elaborate on frequently used datasets and metrics for performance evaluation. We give insights on the historical development and conclude the limitations of off-the-shelf methods. We finally discuss the future directions of VI-ReID that the community should further address.
Highlights
Person re-identification (ReID) is a fundamental building block in various tasks of computer vision, such as intelligent surveillance, video analysis [1], and criminal investigation [2]
Non-generative-based model are dedicated to mitigating the modality gap on the feature-level (e.g., [9]), while generativebased models pay more attention to the pixel level (e.g., [11])
A generative-based model can avoid the impact of color information
Summary
Person re-identification (ReID) is a fundamental building block in various tasks of computer vision, such as intelligent surveillance, video analysis [1], and criminal investigation [2]. With the advancement of intelligent monitoring and the enormous expansion of video data in recent years, conventional human power has been challenging and insufficient to deal with intricate surveillance scenarios. ReID aims at searching for a given individual across disjoint cameras. The images captured by visible cameras may be unavailable in a dark environment. In such a case, infrared imaging equipment, which does not rely on visible light, should be applied
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