mbodSSP [on] [mbodSSP]

Submitted on 1 Nov. 2014 17:30 by
Philip Lenz (KIT)

Running time:0.01 s
Environment:1 core @ 2.7 Ghz (Python)

Method Description:
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.
See paper.
Latex Bibtex:
author = {Philip Lenz and Andreas Geiger and
Raquel Urtasun},
title = {FollowMe: Efficient Online Min-Cost Flow
Tracking with Bounded Memory and Computation},
booktitle = {International Conference on Computer
Vision (ICCV)},
year = {2015}

Detailed Results

From all 29 test sequences, our benchmark computes the commonly used tracking metrics CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2]. The tables below show all of these metrics.

CAR 56.03 % 77.52 % 56.03 % 83.15 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 58.52 % 97.81 % 73.23 % 20682 463 14659 4.16 % 23792 1653

CAR 23.23 % 49.54 % 27.23 % 0 699

This table as LaTeX

[1] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[2] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.

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