Method

TDTracker [TDTracker]
[Anonymous Submission]

Submitted on 28 Mar. 2019 07:36 by
[Anonymous Submission]

Running time:0.03 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
this is a tracking method that it uses multiple
features.
Parameters:
\alpha=0.2
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 68.93 % 77.99 % 69.94 % 83.08 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 79.17 % 92.57 % 85.34 % 30104 2417 7922 21.73 % 35700 1319

Benchmark MT PT ML IDS FRAG
CAR 42.77 % 44.15 % 13.08 % 346 921

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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