  Video-based person re-identification aims to match pedestrians from video
sequences across non-overlapping camera views. The key factor for video person
re-identification is to effectively exploit both spatial and temporal clues
from video sequences. In this work, we propose a novel Spatial-Temporal
Correlation and Topology Learning framework (CTL) to pursue discriminative and
robust representation by modeling cross-scale spatial-temporal correlation.
Specifically, CTL utilizes a CNN backbone and a key-points estimator to extract
semantic local features from human body at multiple granularities as graph
nodes. It explores a context-reinforced topology to construct multi-scale
graphs by considering both global contextual information and physical
connections of human body. Moreover, a 3D graph convolution and a cross-scale
graph convolution are designed, which facilitate direct cross-spacetime and
cross-scale information propagation for capturing hierarchical spatial-temporal
dependencies and structural information. By jointly performing the two
convolutions, CTL effectively mines comprehensive clues that are complementary
with appearance information to enhance representational capacity. Extensive
experiments on two video benchmarks have demonstrated the effectiveness of the
proposed method and the state-of-the-art performance.
