We propose a novel approach to automatically tracking cell populations in
time-lapse images. To account for cell occlusions and overlaps, we introduce a
robust method that generates an over-complete set of competing detection
hypotheses. We then perform detection and tracking simultaneously on these
hypotheses by solving to optimality an integer program with only one type of
flow variables. This eliminates the need for heuristics to handle missed
detections due to occlusions and complex morphology. We demonstrate the
effectiveness of our approach on a range of challenging sequences consisting of
clumped cells and show that it outperforms state-of-the-art techniques.Comment: Engin T\"uretken and Xinchao Wang contributed equally to this wor
