Method

v_L2_g0_e1 [v_L2_g0_e1]
[Anonymous Submission]

Submitted on 17 Jan. 2019 03:16 by
[Anonymous Submission]

Running time:0.1 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
Online multiple object tracker
Parameters:
N:resnet18
A:Vgg
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 78.64 % 85.08 % 80.77 % 89.12 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 84.20 % 97.96 % 90.56 % 31726 660 5954 5.93 % 36296 2058

Benchmark MT PT ML IDS FRAG
CAR 65.69 % 24.31 % 10.00 % 731 1137

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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