SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial Person Re-Identification
This addresses the challenge of occluded person re-identification in surveillance and security applications, representing an incremental improvement by extending holistic methods to handle partial cases.
The paper tackles the problem of person re-identification (ReID) in both holistic and partial scenarios, where occlusions are common, by proposing SCPNet, which learns discriminative features through spatial-channel parallelism. The model achieves competitive accuracy on four holistic datasets and outperforms state-of-the-art methods on two partial datasets without additional training.
Holistic person re-identification (ReID) has received extensive study in the past few years and achieves impressive progress. However, persons are often occluded by obstacles or other persons in practical scenarios, which makes partial person re-identification non-trivial. In this paper, we propose a spatial-channel parallelism network (SCPNet) in which each channel in the ReID feature pays attention to a given spatial part of the body. The spatial-channel corresponding relationship supervises the network to learn discriminative feature for both holistic and partial person re-identification. The single model trained on four holistic ReID datasets achieves competitive accuracy on these four datasets, as well as outperforms the state-of-the-art methods on two partial ReID datasets without training.