A Generalized Successive Shortest Paths Solver for Tracking Dividing Targets

  • Carsten HauboldEmail author
  • Janez Aleš
  • Steffen Wolf
  • Fred A. Hamprecht
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9911)


Tracking-by-detection methods are prevailing in many tracking scenarios. One attractive property is that in the absence of additional constraints they can be solved optimally in polynomial time, e.g. by min-cost flow solvers. But when potentially dividing targets need to be tracked – as is the case for biological tasks like cell tracking – finding the solution to a global tracking-by-detection model is NP-hard. In this work, we present a flow-based approximate solution to a common cell tracking model that allows for objects to merge and split or divide. We build on the successive shortest path min-cost flow algorithm but alter the residual graph such that the flow through the graph obeys division constraints and always represents a feasible tracking solution. By conditioning the residual arc capacities on the flow along logically associated arcs we obtain a polynomial time heuristic that achieves close-to-optimal tracking results while exhibiting a good anytime performance. We also show that our method is a generalization of an approximate dynamic programming cell tracking solver by Magnusson et al. that stood out in the ISBI Cell Tracking Challenges.



This work was partially supported by the HGS MathComp Graduate School, SFB 1129 for integrative analysis of pathogen replication and spread, RTG 1653 for probabilistic graphical models and CellNetworks Excellence Cluster/EcTop.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Carsten Haubold
    • 1
    Email author
  • Janez Aleš
    • 1
  • Steffen Wolf
    • 1
  • Fred A. Hamprecht
    • 1
  1. 1.IWR/HCIUniversity of HeidelbergHeidelbergGermany

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