Cell tracking is a key task in the high-throughput quantitative study of important biological processes, such as immune system regulation and neurogenesis. Variability in cell density and dynamics in different videos, hampers portability of existing trackers across videos. We address these potability challenges in order to develop a portable cell tracking algorithm. Our algorithm can handle noise in cell segmentation as well as divisions and deaths of cells. We also propose a parameter-free variation of our tracker. In the tracker, we employ a novel method for recovering the distribution of cell displacements. Further, we present a mathematically justified procedure for determining the gating distance in relation to tracking performance. For the range of real videos tested, our tracker correctly recovers on average 96% of cell moves, and outperforms an advanced probabilistic tracker when the cell detection quality is high. The scalability of our tracker was tested on synthetic videos with up to 200 cells per frame. For more challenging tracking conditions, we propose a novel semi-automated framework that can increase the ratio of correctly recovered tracks by 12%, through selective manual inspection of only 10% of all frames in a video.