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.

本文提出一种混合数据关联框架，使用最小代价多商品网络流进行在线多目标跟踪，实现了全局优化的数据关联和本地目标特定模型相结合的目标。