Method

A Dynamic-Confidence 3D MOT Framework based on Spatial-Temporal Association [la] [on] [STMOT_v1]


Submitted on 18 May. 2023 05:19 by
Ruihao Zeng (TransportLab, University of Sydney)

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
online tracker
CPU-only
FPS ~311
Parameters:
TBD
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 81.28 % 78.44 % 84.82 % 81.60 % 87.85 % 87.51 % 91.70 % 88.14 %

Benchmark TP FP FN
CAR 31545 2847 401

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 89.83 % 86.94 % 90.56 % 250 77.86 %

Benchmark MT rate PT rate ML rate FRAG
CAR 80.92 % 9.85 % 9.23 % 52

Benchmark # Dets # Tracks
CAR 31946 864

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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