Method

Smart3DMOT: Multi-modal 3D multi-object tracking based on dual cascade matching strategy [Smart3DMOT]
[Anonymous Submission]

Submitted on 16 May. 2023 14:58 by
[Anonymous Submission]

Running time:2min
Environment:1 core @ 2.5 Ghz (C/C++) GPU@Nvidia3090

Method Description:
we introduce Smart3DMOT, an innovative 3D MOT
algorithm that effectively associates multiple
objects by employing a dual-cascade matching
strategy in various fused 3D detection forms.
Parameters:
variables η and δ are assigned the values of 4 and
0.5, respectively. In equation 20, γ1 and γ2 are
redefined as -0.7 and 1.2, respectively.
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 85.99 % 85.09 % 86.24 % 88.39 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 88.40 % 99.10 % 93.44 % 33718 307 4425 2.76 % 37201 757

Benchmark MT PT ML IDS FRAG
CAR 69.38 % 24.46 % 6.15 % 87 489

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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