Method

BiTrack [la] [BiTrack]


Submitted on 27 May. 2023 11:15 by
Kemiao Huang (Southern University of Science and Technology)

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
offline MOT from VirConv detections
Parameters:
TBD
Latex Bibtex:
TBD

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 82.69 % 80.13 % 86.07 % 84.71 % 87.08 % 89.03 % 92.11 % 88.65 %

Benchmark TP FP FN
CAR 32500 1892 959

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 91.65 % 87.53 % 91.71 % 21 79.87 %

Benchmark MT rate PT rate ML rate FRAG
CAR 86.00 % 8.62 % 5.38 % 278

Benchmark # Dets # Tracks
CAR 33459 674

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