In this paper, we consider a multi-object tracking problem in complex scenes. Unlike batch tracking systems using detections of the entire sequence, we propose a novel online multi-object tracking system in order to build tracks sequentially using online provided detections. To track objects robustly even under frequent occlusions, the proposed system consists of three main parts: (1) Visual tracking with a novel data association with a track existence probability by associating online detections with the corresponding tracks under partial occlusions, (2) Track management to associate terminated tracks for linking tracks fragmented by long-term occlusions, (3) Online model learning to generate discriminative appearance models for successful associations in other two parts. Experimental results using challenging public datasets show the obvious performance improvement of the proposed system, compared to other state-of-the-art tracking systems. Furthermore, extensive performance analysis of the three main parts demonstrates effects and usefulness of the each component for multi-object tracking.