It is difficult for a human operator to monitor multiple video streams in real-time surveillance tasks. A variety of machine learning algorithms have been proposed for target tracking, but human intervention is still necessary because the algorithms have limitations to detect and recover from tracking failures. In this paper, we propose a supervisory target tracking method for multi-video surveillance tasks based on hTLD (human-in-the-loop Tracking-Learning-Detection). The human operator can monitor both the original video streams and the calculated confidence values of the tracking results. Human intervention allows to update the training data for TLD whenever a tracking failure is observed, by manually drawing a bounding box or using an eye-tracker to relocate the target. Meanwhile, an interactive on-line learning algorithm is used to learn the relations between the confidence values and the tracking results. In this way, the confidence values help avoid unnecessary intervention caused by false alarms of tracking failures. Experimental results have demonstrated that the proposed method can reduce the task completion time.