Person re-identification (re-ID) is highly complex in a diverse surveillance environment. The existing person re-ID methods are evaluated as a closed set problem with limited environmental variation. It is highly challenging to estimate the diverse poses of a dynamically crowded environment using the traditional unsupervised person re-ID methods. To resolve this issue of handling complex diverse poses and camera angles, a contextual incremental multi-clustering based unsupervised person re-ID method have been proposed. Cam-pose based optimal similarity distance threshold is determined to label the unlabeled person re-ID images efficiently. Frequent intra and inter-camera pseudo pose sequences are represented with optimal distance threshold. This resolves the over-fitting issue created by the dominant samples of an identity and reduces the source-target domain gap. The experimental results show the supremacy of our proposed method over the existing unsupervised person re-ID methods in handling complex poses and camera angles in an incremental self-learning diverse surveillance environment.

在不同的监视环境中，人员重新识别（re-ID）非常复杂。现有的人员重新识别方法被评估为环境变化有限的封闭问题。使用传统的无监督人员re-ID方法来估计动态拥挤环境的各种姿势是非常具有挑战性的。为了解决这个处理复杂的多样姿势和摄像机角度的问题，提出了一种基于上下文增量多聚类的无监督人重新识别方法。确定基于凸轮姿势的最佳相似性距离阈值以有效地标记未标记的人re-ID图像。摄像机内部和摄像机之间的伪伪姿势序列通常以最佳距离阈值表示。这解决了由身份的主要样本造成的过度拟合问题，并减少了源-目标域之间的差距。实验结果表明，相比于现有的无监督人员re-ID方法，我们的方法在处理增量式自学习多样化监视环境中的复杂姿势和摄像机角度方面具有优越性。