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 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 74.64 % 72.36 % 77.62 % 75.33 % 86.06 % 80.22 % 89.70 % 86.59 %

Benchmark TP FP FN
CAR 29784 4608 319

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 85.29 % 85.03 % 85.67 % 133 72.32 %

Benchmark MT rate PT rate ML rate FRAG
CAR 67.85 % 25.39 % 6.77 % 488

Benchmark # Dets # Tracks
CAR 30103 680

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.


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