Title: UniTrack: Differentiable Graph Representation Learning for Multi-Object Tracking
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Multi-object tracking, graph representation learning, differentiable optimization, end-to-end learning, identity preservation, spatio-temporal modeling, flow networks, unified loss functions, video understanding, deep learning

Abstract: We present UniTrack, a novel differentiable graph representation learning framework designed to significantly enhance multi-object tracking (MOT) performance by directly optimizing tracking-specific objectives through unified graph-theoretic learning. UniTrack learns joint representations that integrate detection accuracy, identity preservation, and spatiotemporal consistency into a single end-to-end trainable objective to resolve dominant MOT failures. Through differentiable graph representation learning, UniTrack enables networks to learn holistic representations of motion continuity and identity relationships across frames, offering a generalizable, plug-and-play solution for enhancing MOT systems in both accuracy and robustness. We validate UniTrack across diverse tracking models and multiple challenging benchmarks, demonstrating consistent improvements across all tested architectures and datasets including Trackformer, MOTR, FairMOT, ByteTrack, GTR and MOTE. Extensive evaluations show up to 53\% reduction in identity switches and 12\% IDF1 improvements across challenging benchmarks, with GTR achieving peak performance gains of 9.7\% MOTA on SportsMOT.

TL;DR: UniTrack is a differentiable graph representation learning framework that unifies detection, identity preservation, and temporal consistency for improved multi-object tracking.