A Generalized Successive Shortest Paths Solver for Tracking Dividing Targets

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

Abstract

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.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

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

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