Method

Strong Geometric Association Model [SG-AM]
[Anonymous Submission]

Submitted on 10 Apr. 2023 10:09 by
[Anonymous Submission]

Running time:248 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
The SG-AM model implements robust 3D geometric
constraints to associate objects across frames
Parameters:
using both camera and lidar data
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 85.50 % 85.11 % 85.80 % 88.42 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 87.94 % 99.12 % 93.20 % 33452 298 4587 2.68 % 36813 790

Benchmark MT PT ML IDS FRAG
CAR 68.46 % 25.38 % 6.15 % 101 523

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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