Multiple Object Tracking (MOT) is an important computer vision task and many MOT issues are still unsolved. Factors such as occlusions, illumination, object densities are big challenges for MOT. Therefore, this thesis proposes three MOT approaches to handle these challenges. The proposed approaches can be distinguished through two properties: their generality and their effectiveness.The first approach selects automatically the most reliable features to characterize each tracklet in a video scene. No training process is needed which makes this algorithm generic and deployable within a large variety of tracking frameworks. The second method tunes online tracking parameters for each tracklet according to the variation of the tracklet's surrounding context. There is no requirement on the number of tunable tracking parameters as well as their mutual dependence in the learning process. However, there is a need of training data which should be representative enough to make this algorithm generic. The third approach takes full advantage of features (hand-crafted and learned features) and tracklet affinity measurements proposed for the Re-id task and adapting them to MOT. Framework can work with or without training step depending on the tracklet affinity measurement.The experiments over three datasets, MOT2015, MOT2017 and ParkingLot show that the third approach is the most effective. The first and the third (without training) approaches are the most generic while the third approach (with training) necessitates the most supervision. Therefore, depending on the application as well as the availability of a training dataset, the most appropriate MOT algorithm could be selected.