Method

Fast Fusion Multi object Tracking [st] [la] [FFMOT]


Submitted on 10 Mar. 2023 14:14 by
Lu Yu (Tongji University)

Running time:0.01 s
Environment:GPU @ 2.0 Ghz (Python)

Method Description:
This method is a simple tracking formulation that integrates object observations from both sensor modalities.
Parameters:
None
Latex Bibtex:
None

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 73.38 % 74.93 % 72.54 % 81.13 % 84.17 % 75.84 % 90.54 % 88.11 %

Benchmark TP FP FN
CAR 31406 2986 1742

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 85.75 % 86.86 % 86.25 % 173 73.75 %

Benchmark MT rate PT rate ML rate FRAG
CAR 74.77 % 20.61 % 4.62 % 215

Benchmark # Dets # Tracks
CAR 33148 963

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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