Method

[la]Factor Graph based 3D Multi-Object Tracking in Point Clouds [FG-3DMOT]


Submitted on 4 Mar. 2020 15:22 by
Johannes Poeschmann (TU Chemnitz)

Running time:0.04 s
Environment:4 cores @ 3.5 Ghz (C/C++)

Method Description:
We propose a novel optimization-based approach that does not rely on explicit and fixed assignments. Instead, we describe the 3D multi-object state estimation problem using a factor graph framework, which gives us the flexibility to assign all detections to all objects simultaneously. This is achieved, by representing the result of an off-the-shelf 3D object detector (PointRCNN) as Gaussian mixture model, which can be incorporated in the factor graph. The assignment problem is then solved implicitly and jointly with the spatial state estimation using non-linear least squares optimization.
Parameters:
Latex Bibtex:

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.


Benchmark MOTA MOTP MODA MODP
CAR 88.01 % 85.04 % 88.07 % 88.39 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 90.70 % 98.24 % 94.32 % 34052 611 3491 5.49 % 38914 778

Benchmark MT PT ML IDS FRAG
CAR 75.54 % 12.62 % 11.85 % 20 117

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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