Method

tracking with few-labeled frames [tflf]
[Anonymous Submission]

Submitted on 18 Oct. 2024 03:20 by
[Anonymous Submission]

Running time:35 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
multiple object tracking with few-labeled frames.
Parameters:
\alpha=0.5
Latex Bibtex:

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 -101.19 % 59.48 % -101.19 % 100.00 %
PEDESTRIAN -60.06 % 0.00 % 0.00 % 0.00 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 0.12 % 0.11 % 0.12 % 40 34803 34392 312.86 % 38255 1395
PEDESTRIAN 0.00 % 0.00 % 0.00 % 0 0 0 0.00 % 0 0

Benchmark MT PT ML IDS FRAG
CAR 0.00 % 0.00 % 100.00 % 0 0
PEDESTRIAN 0.00 % 0.00 % 0.00 % 0 0

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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