行人再识别技术研究进展

Person re-Identification（person re-ID）technique aims to solve the problem of association and matching of target person images across multiple cameras within a camera network of a surveillance system，especially in the case of face，iris and other biometrics recognition failure under non-cooperative application scenarios，and has become one of the key component and supporting technique for intelligent video surveillance systems and applications in intelligent public security and smart cities. Recently，person re-ID has attracted more and more attention from both academia and industry，and has made rapid development and progress. To meet the requirement of its technical challenges and application needs of person re-ID in practical scenarios，this paper will first give a brief introduction to the development history，commonly used datasets and evaluation metrics. Then，the recent progress in hot research topics of person re-ID is extensively reviewed and analyzed，which includes：occluded person re-ID，unsupervised person re-ID，virtual data generation，domain generalization，cloth-changing person re-ID，cross-modal person re-ID and person search. First，to address the problem of impact of possible occlusions on the performance of person re-ID，recent progress in occluded person re-ID is first reviewed，in which the popular datasets for occluded person re-ID are briefly introduced，and the two major categories of occluded person re-ID models are then further reviewed. Second，facing the challenges of low-efficiency and high-cost data annotation and great impact of training data on the performance of person re-ID，unsupervised person re-ID and virtual data generation for person re-ID emerges as two hot topics in person re-ID. The paper elaborates the recent advances of unsupervised person reID，which can be classified into three major categories：pseudo label generation-based models，domain transfer-based models，and other related models，which take into consideration of the extra information like time-stamps，camera labels besides person image. Third，the-state-of-the-art works on virtual data generation for person re-ID are reviewed，with detailed introduction and performance comparisons of major virtual datasets. Fourth，recent researches on domain generalization person re-ID will be reviewed，which are classified into five categories：batch/instance normalization models，domain invariant feature learning models，deep-learning-based explicit image matching models，models based on mixture of experts and meta-learning-based models. Fifth，since most current person re-ID models largely depend on the color appearance of persons’clothes，clothes-changing person re-ID becomes a challenging setting，in which person images can exhibit large intra-class variation and small inter-class variation. Typical cloth-changing person re-ID datasets are introduced and the recent researches will then be reviewed，in which models in the first category explicitly introduces extra cloth-appearance-independent features like contour and face while the second try to decouple the cloth features and person ID features. Sixth，to compensate the drawbacks of conventional person re-ID of visible light/RGB images in natural complex scenes like poor lighting conditions in the night，the-state-of-the-art of cross-modal person re-ID，which aims to resolve the problem through other visible RGB images-excluded heterogeneous data，are reviewed，with a brief introduction of commonly used cross-modal person re-ID datasets first and then four sub-categories models according to the different modalities employed，including：RGB-infrared image person re-ID，RGB image-text person re-ID，RGB image-sketch person re-ID，and RGB-depth image person re-ID，respectively. Seventh，since existing person re-ID benchmarks and methods mainly focus on matching cropped person images between queries and candidates and is different from practical scenarios where the bounding box annotations of persons are often unavailable ，person search，which jointly considers person detection and person re-ID in a single framework，becomes a new hot research topic. The typical datasets and recent progress on person search are reviewed. Finally，the existing challenges and development trend of person re-ID techniques are discussed. It is hoped that the summary and analysis can provide reference for relevant researchers to carry out research on person re-ID and promote the progress of person re-ID techniques and applications.