Monocular 3D Multi-Object Tracking with an EKF Approach for Long-Term Stable Tracks [gp][on] [Mono_3D_KF]

Submitted on 4 May. 2021 16:51 by
Andreas Reich (Universität der Bundeswehr München)

Running time:0.3 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
Most monocular object tracking algorithms work
in the 2D domain of the image. However, object trajectories,
which are very simple in a fixed 3D world space, result in
complex motions on the image plane, especially when the camera
is moving. Therefore, in absence of any 3D representation, afore-
mentioned approaches are only able to perform the measurement-
to-track association based on rough similarity of 2D bounding box
parameters. Recent advances in monocular 3D object detection
allow to extract additional parameters like the pose and spatial
extent of a 3D bounding box. In this paper, we present a multi-
object tracking approach composed of an Extended Kalman
filter estimating the 3D state by using these detections for track
initialization. In subsequent time steps 2D bounding boxes are
used to avoid filtering temporally correlated 3D measurements.
This ensures properly estimated state uncertainties. (For more see paper)
Will be provided with Paper
Latex Bibtex:

author={Reich, Andreas and Wuensche, Hans-Joachim},

booktitle={2021 IEEE 24th International Conference on Information Fusion (FUSION)},

title={Monocular 3D Multi-Object Tracking with an EKF Approach for Long-Term Stable Tracks},






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 75.47 % 74.10 % 77.63 % 78.86 % 82.98 % 80.23 % 88.88 % 85.48 %
PEDESTRIAN 42.87 % 40.13 % 46.31 % 46.02 % 59.91 % 52.86 % 63.50 % 74.03 %

Benchmark TP FP FN
CAR 31638 2754 1045
PEDESTRIAN 14285 8865 3498

CAR 88.48 % 83.70 % 88.95 % 162 73.49 %
PEDESTRIAN 45.44 % 69.06 % 46.60 % 267 26.35 %

Benchmark MT rate PT rate ML rate FRAG
CAR 80.61 % 15.23 % 4.15 % 153
PEDESTRIAN 33.68 % 39.86 % 26.46 % 655

Benchmark # Dets # Tracks
CAR 32683 868
PEDESTRIAN 17783 460

This table as LaTeX

This figure as: png pdf

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.

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