Method

MAE per-training 3D multiobject tracking for autonomous driving [la] [3DMAETracking]
[Anonymous Submission]

Submitted on 17 Jul. 2023 16:40 by
[Anonymous Submission]

Running time:34 s
Environment:>8 cores @ 2.5 Ghz (Python)

Method Description:
MAE Pre-training 3D multiobject tracking for on
LiDAR Point Clouds
Parameters:
None
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 70.45 % 70.76 % 70.70 % 74.19 % 85.34 % 73.73 % 87.64 % 86.70 %

Benchmark TP FP FN
CAR 29297 5095 601

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 82.72 % 85.17 % 83.44 % 248 70.09 %

Benchmark MT rate PT rate ML rate FRAG
CAR 62.92 % 29.69 % 7.38 % 224

Benchmark # Dets # Tracks
CAR 29898 938

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