Existing person re-identification (re-ID) research mainly focuses on pedestrian identity matching across cameras in adjacent areas. However, in reality, it is inevitable to face the problem of pedestrian identity matching across long-distance scenes. The cross-camera pedestrian samples collected from long-distance scenes often have no positive samples. It is extremely challenging to use cross-camera negative samples to achieve cross-region pedestrian identity matching. Therefore, a novel domain-adaptive person re-ID method that focuses on cross-camera consistent discriminative feature learning under the supervision of unpaired samples is proposed. This method mainly includes category synergy co-promotion module (CSCM) and cross-camera consistent feature learning module (CCFLM). In CSCM, a task-specific feature recombination (FRT) mechanism is proposed. This mechanism first groups features according to their contributions to specific tasks. Then an interactive promotion learning (IPL) scheme between feature groups is developed and embedded in this mechanism to enhance feature discriminability. Since the control parameters of the specific task model are reduced after division by task, the generalization ability of the model is improved. In CCFLM, instance-level feature distribution alignment and cross-camera identity consistent learning methods are constructed. Therefore, the supervised model training is achieved under the style supervision of the target domain by exchanging styles between source-domain samples and target-domain samples, and the challenges caused by the lack of cross-camera paired samples are solved by utilizing cross-camera similar samples. In experiments, three challenging datasets are used as target domains, and the effectiveness of the proposed method is demonstrated through four experimental settings.

翻译：现有行人重识别研究主要关注相邻区域内跨相机的行人身份匹配。然而实际应用中不可避免地面临远距离场景下的行人身份匹配问题，此时采集的跨相机行人样本往往不存在正样本。如何利用跨相机负样本实现跨区域行人身份匹配极具挑战性。为此，本文提出一种面向无配对样本监督的跨相机一致判别特征学习的域自适应行人重识别新方法，主要包括类别协同共促模块和跨相机一致特征学习模块。在类别协同共促模块中，提出任务特征重组机制：首先根据特征对特定任务的贡献度进行特征分组，继而开发并嵌入特征组间的交互促进学习方案以增强特征判别力。由于按任务划分后特定任务模型的控制参数减少，模型泛化能力得到提升。在跨相机一致特征学习模块中，构建实例级特征分布对齐与跨相机身份一致学习方法，通过源域样本与目标域样本间的风格交换实现目标域风格监督下的模型训练，并利用跨相机相似样本解决缺乏跨相机配对样本带来的挑战。实验采用三个具有挑战性的数据集作为目标域，通过四组实验设置验证了所提方法的有效性。