  Video-based person re-identification (re-ID) is an important research topic
in computer vision. The key to tackling the challenging task is to exploit both
spatial and temporal clues in video sequences. In this work, we propose a novel
graph-based framework, namely Multi-Granular Hypergraph (MGH), to pursue better
representational capabilities by modeling spatiotemporal dependencies in terms
of multiple granularities. Specifically, hypergraphs with different spatial
granularities are constructed using various levels of part-based features
across the video sequence. In each hypergraph, different temporal granularities
are captured by hyperedges that connect a set of graph nodes (i.e., part-based
features) across different temporal ranges. Two critical issues (misalignment
and occlusion) are explicitly addressed by the proposed hypergraph propagation
and feature aggregation schemes. Finally, we further enhance the overall video
representation by learning more diversified graph-level representations of
multiple granularities based on mutual information minimization. Extensive
experiments on three widely adopted benchmarks clearly demonstrate the
effectiveness of the proposed framework. Notably, 90.0% top-1 accuracy on MARS
is achieved using MGH, outperforming the state-of-the-arts. Code is available
at https://github.com/daodaofr/hypergraph_reid.
