Kinematic Structure Correspondences via Hypergraph Matching
Hyung Jin Chang Tobias Fischer Maxime Petit Martina Zambelli Yiannis Demiris
Imperial College London
In this paper, we present a novel framework for finding the kinematic structure correspondence between two objects in videos via hypergraph matching. In contrast to prior appearance and graph alignment based matching methods which have been applied among two similar static images, the proposed method finds correspondences between two dynamic kinematic structures of heterogeneous objects in videos. Our main contributions can be summarised as follows: (i) casting the kinematic structure correspondence problem into a hypergraph matching problem, incorporating multi-order similarities with normalising weights, (ii) structural topology similarity measure by a new topology constrained subgraph isomorphism aggregation, (iii) kinematic correlation measure between pairwise nodes, (iv) combinatorial local motion similarity measure using geodesic distance on the Riemannian manifold. We demonstrate the robustness and accuracy of our method through a number of experiments on synthetic and real data, showing that various other methods are out-performed.
[Dataset & Code]
Imperial-PRL KSC Dataset [Download]