A Hybrid Data Association Framework for Robust Online Multi-Object Tracking
This work addresses the difficulty of handling online data in multi-object tracking, which is important for applications like surveillance and autonomous driving, but it appears incremental as it builds on existing global optimization methods.
The paper tackles the problem of robust online multi-object tracking by proposing a hybrid data association framework that combines local target-specific models with global optimization using a min-cost multi-commodity network flow. The result is superior tracking performance demonstrated through comprehensive experiments on real data in challenging situations.
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.