Human skeleton mutual learning for person re-identification

Publication date: Available online 16 January 2020Source: NeurocomputingAuthor(s): Ziyang Wang, Dan Wei, Xiaoqiang Hu, Yiping LuoAbstractPerson re-identification refers to matching people across non-overlapping camera views on different locations and at different times. In the case of changes in perspective, light, background, veil, and person's clothing, traditional method can't achieve person recognition effectively and reliably. In this paper, we propose a novel biometric metric learning method named Human Skeleton Mutual Learning person re-identification (HSMLP-Reid). The purpose of HSML person re-identification method (HSMLP-Reid) largely aims to use the new pedestrian local segmentation method proposed in this paper combined with the global skeleton information to solve the influence of background and local posture change. Firstly, bottom-up method is used to estimate the pedestrian posture and skeleton, and the joint points of the pedestrians will be marked in this process. A new local segmentation method proposed in this paper named joint segmentation is used to locally segment pedestrians and perform local block matching. Furthermore, we learn the global skeleton information by defined joint distances from the pedestrian 2D skeleton estimation by the bottom-up method and use global skeleton information to global skeleton matching. Finally, we use local match and global skeleton match for mutual learning. Local match based on pedestrian nodes and global skeleton match...

Source: Neurocomputing - January 16, 2020 Category: Neuroscience Source Type: research