# A Hybrid Data Association Framework for Robust Online Multi-Object Tracking

## TL;DR

This paper introduces a hybrid data association framework utilizing min-cost multi-commodity network flow for robust online multi-object tracking, combining local target models with global optimization to improve accuracy and efficiency.

## Contribution

It proposes a novel hybrid framework that integrates local target-specific models with global data association, along with an efficient near-optimal solution for real-time tracking.

## Key findings

- Superior tracking performance demonstrated on real datasets
- Effective reduction of local association errors
- Robust handling of challenging tracking scenarios

## Abstract

Global optimization algorithms have shown impressive performance in data-association based multi-object tracking, but handling online data remains a difficult hurdle to overcome. In this paper, we present a hybrid data association framework with a min-cost multi-commodity network flow for robust online multi-object tracking. We build local target-specific models interleaved with global optimization of the optimal data association over multiple video frames. More specifically, in the min-cost multi-commodity network flow, the target-specific similarities are online learned to enforce the local consistency for reducing the complexity of the global data association. Meanwhile, the global data association taking multiple video frames into account alleviates irrecoverable errors caused by the local data association between adjacent frames. To ensure the efficiency of online tracking, we give an efficient near-optimal solution to the proposed min-cost multi-commodity flow problem, and provide the empirical proof of its sub-optimality. The comprehensive experiments on real data demonstrate the superior tracking performance of our approach in various challenging situations.