Method

Learning Pseudo 3D Representation for Ego-centric 2D Multiple Object Tracking [P3DTrack]
[Anonymous Submission]

Submitted on 9 Aug. 2023 04:36 by
[Anonymous Submission]

Running time:0.1 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
Anonymous
Parameters:
Anonymous
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 74.59 % 72.88 % 76.86 % 78.09 % 83.21 % 80.66 % 86.67 % 86.28 %

Benchmark TP FP FN
CAR 30968 3424 1308

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 85.60 % 84.58 % 86.24 % 219 71.72 %

Benchmark MT rate PT rate ML rate FRAG
CAR 73.69 % 21.85 % 4.46 % 173

Benchmark # Dets # Tracks
CAR 32276 707

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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