Method

dsf [dst]
[Anonymous Submission]

Submitted on 15 Nov. 2022 02:55 by
[Anonymous Submission]

Running time:15 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
3243243234
Parameters:
234234
Latex Bibtex:
234234

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
PEDESTRIAN 61.80 % 73.21 % 63.94 % 92.09 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
PEDESTRIAN 75.05 % 87.61 % 80.84 % 17616 2492 5857 22.40 % 22937 368

Benchmark MT PT ML IDS FRAG
PEDESTRIAN 48.45 % 30.58 % 20.96 % 494 1027

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.


eXTReMe Tracker