Deep manifold clustering based optimal pseudo pose representation (DMC-OPPR) for unsupervised person re-identification

Abstract

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.