Method

McByte - mask-cued ByteTrack [McByte]
[Anonymous Submission]

Submitted on 8 May. 2024 20:14 by
[Anonymous Submission]

Running time:99 min
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
(Will be provided upon the paper acceptance)
Parameters:
As described in the paper (to be published)
Latex Bibtex:
(To be provided after the publication/submission)

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 79.94 % 77.36 % 83.17 % 80.69 % 86.44 % 86.14 % 89.38 % 86.97 %
PEDESTRIAN 55.65 % 51.75 % 60.08 % 55.97 % 72.26 % 65.14 % 73.89 % 78.51 %

Benchmark TP FP FN
CAR 31744 2648 362
PEDESTRIAN 16750 6400 1180

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 91.06 % 85.48 % 91.25 % 64 77.66 %
PEDESTRIAN 66.79 % 74.56 % 67.26 % 108 48.38 %

Benchmark MT rate PT rate ML rate FRAG
CAR 79.54 % 16.46 % 4.00 % 85
PEDESTRIAN 40.21 % 37.46 % 22.34 % 439

Benchmark # Dets # Tracks
CAR 32106 670
PEDESTRIAN 17930 270

This table as LaTeX

This figure as: png pdf

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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