Perceiving Systems, Computer Vision

FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation

2015

Conference Paper

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One of the most popular approaches to multi-target tracking is tracking-by-detection. Current min-cost flow algorithms which solve the data association problem optimally have three main drawbacks: they are computationally expensive, they assume that the whole video is given as a batch, and they scale badly in memory and computation with the length of the video sequence. In this paper, we address each of these issues, resulting in a computationally and memory-bounded solution. First, we introduce a dynamic version of the successive shortest-path algorithm which solves the data association problem optimally while reusing computation, resulting in faster inference than standard solvers. Second, we address the optimal solution to the data association problem when dealing with an incoming stream of data (i.e., online setting). Finally, we present our main contribution which is an approximate online solution with bounded memory and computation which is capable of handling videos of arbitrary length while performing tracking in real time. We demonstrate the effectiveness of our algorithms on the KITTI and PETS2009 benchmarks and show state-of-the-art performance, while being significantly faster than existing solvers.

Author(s): Philip Lenz and Andreas Geiger and Raquel Urtasun
Book Title: International Conference on Computer Vision (ICCV)
Year: 2015
Month: December

Department(s): Autonomous Vision, Perceiving Systems
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

Event Name: International Conference on Computer Vision (ICCV)
Event Place: Santiago, Chile

State: Published

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BibTex

@inproceedings{Lenz2015ICCV,
  title = {FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation},
  author = {Lenz, Philip and Geiger, Andreas and Urtasun, Raquel},
  booktitle = {International Conference on Computer Vision (ICCV)},
  month = dec,
  year = {2015},
  doi = {},
  month_numeric = {12}
}