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 commonly used tracking metrics CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2]. The tables below show all of these metrics.


Benchmark MOTA MOTP MODA MODP
CAR 91.05 % 85.71 % 91.30 % 88.42 %
PEDESTRIAN 65.52 % 74.69 % 66.25 % 92.25 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 93.28 % 99.00 % 96.06 % 36446 368 2624 3.31 % 44115 820
PEDESTRIAN 72.73 % 92.16 % 81.30 % 16986 1444 6369 12.98 % 21796 312

Benchmark MT PT ML IDS FRAG
CAR 80.15 % 15.85 % 4.00 % 85 151
PEDESTRIAN 40.55 % 37.46 % 21.99 % 170 674

This table as LaTeX

[1] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[2] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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