  Lifelong person re-identification (LReID) is in significant demand for
real-world development as a large amount of ReID data is captured from diverse
locations over time and cannot be accessed at once inherently. However, a key
challenge for LReID is how to incrementally preserve old knowledge and
gradually add new capabilities to the system. Unlike most existing LReID
methods, which mainly focus on dealing with catastrophic forgetting, our focus
is on a more challenging problem, which is, not only trying to reduce the
forgetting on old tasks but also aiming to improve the model performance on
both new and old tasks during the lifelong learning process. Inspired by the
biological process of human cognition where the somatosensory neocortex and the
hippocampus work together in memory consolidation, we formulated a model called
Knowledge Refreshing and Consolidation (KRC) that achieves both positive
forward and backward transfer. More specifically, a knowledge refreshing scheme
is incorporated with the knowledge rehearsal mechanism to enable bi-directional
knowledge transfer by introducing a dynamic memory model and an adaptive
working model. Moreover, a knowledge consolidation scheme operating on the dual
space further improves model stability over the long term. Extensive
evaluations show KRC's superiority over the state-of-the-art LReID methods on
challenging pedestrian benchmarks.
