ABSTRACT

Person re-identification (Re-ID) can determine whether a pedestrian target can be matched across diverse regions or cameras, thereby alleviating the problem between massive surveillance data and inefficient manual retrieval. Inspired by attribute-person recognition (APR) network, this paper proposes an improved Re-ID method based on attribute learning, which uses an attribute-guided attention mechanism module and an attribute dependency learning module to learn fine-grained attribute features and rich dependencies among them. After that, a joint model with the integration of attribute recognition and person identity recognition is built for end-to-end training. Experimental results show that the proposed method can effectively improve Re-ID accuracy and achieve a competitive recognition performance.

摘要

行人重识别 (Re-ID) 可以确定行人目标是否可以跨不同区域或摄像机进行匹配，从而缓解海量监控数据和低效的人工检索之间的问题。受属性人识别（APR）网络的启发，本文提出了一种改进的基于属性学习的Re-ID方法，该方法使用属性引导的注意机制模块和属性依赖学习模块来学习细粒度的属性特征和丰富的依赖关系他们之中。之后，构建了一个融合属性识别和人物身份识别的联合模型进行端到端训练。实验结果表明，所提出的方法可以有效提高Re-ID的准确率，达到具有竞争力的识别性能。