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 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 76.20 % 72.69 % 80.46 % 75.72 % 86.02 % 83.23 % 89.50 % 86.58 %

Benchmark TP FP FN
CAR 29947 4445 327

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 85.83 % 85.01 % 86.12 % 101 72.78 %

Benchmark MT rate PT rate ML rate FRAG
CAR 68.61 % 24.61 % 6.77 % 463

Benchmark # Dets # Tracks
CAR 30274 660

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.


eXTReMe Tracker