Geometric Deep Learning for Multi-Object Tracking: A Brief Review

Graphs frequently appear as a type of data structure that efficiently models a set of interrelated objects as nodes and their relations as edges between them. Geometric deep learning emerged as a promising field for modeling non-Euclidean geometric data as graphs, Riemannian manifolds and meshes. In this paper, we review geometric deep learning based state-of-the-art approaches for multi-object tracking. We illustrate the main concept, the model architecture, loss functions, and optimization strategies of the main algorithms. Moreover, algorithms are quantitatively evaluated on standard performance metrics using the standard dataset. The potential of geometric deep learning in tracking is discussed and future directions are proposed.