Appearance based multi-object tracking (MOT) is a challenging task, specially in complex scenes where objects have similar appearance or are occluded by background or other objects. Such factors motivate researchers to propose effective trackers which should satisfy real-time processing and object trajectory recovery criteria. In order to handle both mentioned requirements, we propose a robust online multi-object tracking method that extends the features and methods proposed for re-identification to MOT. The proposed tracker combines a local and a global tracker in a comprehensive two-step framework. In the local tracking step, we use the frame-to-frame association to generate online object trajectories. Each object trajectory is called tracklet and is represented by a set of multi-modal feature distributions modeled by GMMs. In the global tracking step, occlusions and mis-detections are recovered by tracklet bipartite association method based on learning Mahalanobis metric between GMM components using KISSME metric learning algorithm. Experiments on two public datasets show that our tracker performs well when compared to state-of-the-art tracking algorithms.