"In this paper, we focus on developing a novel visual target tracking system (TRACTOR) that enables reliable operation in realistic tracking scenarios. Although much progress has been made in the field of visual target tracking, there are still challenging scenarios in which even state-of-the-art trackers do not operate reliably. For instance, most trackers are prone to drift if a target moves abruptly in unexpected directions, or reappears after being fully occluded by the clutters or disappeared from the field of view of a camera. To cope with these scenarios effectively, the proposed tracking system subdivides the task of visual target tracking into two subtasks, i.e., tracking and detection. 1) For target-visible frames, the tracker builds a collaborative framework with the proposed appearance, observation, and motion models thereby achieving robust performance against unexpected motions and appearance changes of a target and 2) for target-invisible frames, the detector verifies continuously whether a target candidate is the lost target or clutter thereby reducing false target alarms effectively. In extensive experiments, the proposed tracking system shows very promising performance in comparison with state-of-the-art tracking methods."