Anonymous Submission [SSP*]

Submitted on 1 Nov. 2014 20:52 by
Philip Lenz (KIT)

Running time:0.6 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.
* Regionlets were used as detections
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 HOTA tracking metrics (HOTA, DetA, AssA, DetRe, DetPr, AssRe, AssPr, LocA) [1] as well as the CLEARMOT, MT/PT/ML, identity switches, and fragmentation [2,3] metrics. The tables below show all of these metrics.

Benchmark HOTA DetA AssA DetRe DetPr AssRe AssPr LocA
CAR 51.16 % 58.96 % 44.64 % 65.26 % 74.09 % 46.75 % 80.78 % 81.32 %

Benchmark TP FP FN
CAR 27743 6649 2549

CAR 70.94 % 78.64 % 73.25 % 795 53.71 %

Benchmark MT rate PT rate ML rate FRAG
CAR 54.46 % 36.77 % 8.77 % 786

Benchmark # Dets # Tracks
CAR 30292 1838

This table as LaTeX

This figure as: png pdf

[1] J. Luiten, A. Os̆ep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixé, B. Leibe: HOTA: A Higher Order Metric for Evaluating Multi-object Tracking. IJCV 2020.
[2] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[3] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.

eXTReMe Tracker