  This paper proposes a novel method for online Multi-Object Tracking (MOT)
using Graph Convolutional Neural Network (GCNN) based feature extraction and
end-to-end feature matching for object association. The Graph based approach
incorporates both appearance and geometry of objects at past frames as well as
the current frame into the task of feature learning. This new paradigm enables
the network to leverage the "context" information of the geometry of objects
and allows us to model the interactions among the features of multiple objects.
Another central innovation of our proposed framework is the use of the Sinkhorn
algorithm for end-to-end learning of the associations among objects during
model training. The network is trained to predict object associations by taking
into account constraints specific to the MOT task. Experimental results
demonstrate the efficacy of the proposed approach in achieving top performance
on the MOT '15, '16, '17 and '20 Challenges among state-of-the-art online
approaches. The code is available at https://github.com/IPapakis/GCNNMatch.
