Multi object tracking is one interesting topics of computer science that has many applications, such as surveillance system, navigation robot, sports analysis, autonomous driving car, and others. One of the main task of multi-object tracking is data association. This study discusses data association on multi-object tracking and its completion with a two-layer network flow approach. Notice that each object on a frame as a node, then there is an edge connecting each node from one frame to other frame and then finding for the set of edges that provide the greatest probability of transition from one frame to the next, or in the optimization problem better known as max-cost network flow. The probability calculation is based on position distance and similarity feature between objects. The data used in this research is 2DMOT2015. The proposed method obtains highly competitive MOTA of 20.1% compared to existing method with fast computation speed by 215.8 fps.