Method

IWNCC [on] [IWNCC]


Submitted on 6 Jan. 2019 05:48 by
Yuki Tsuji (Tokyo University)

Running time:0.01s
Environment:1 core @ 2.5 Ghz (Python + C/C++)

Method Description:
Real-Time multiple object tracker
Parameters:
V: 0.5
M: 0.3
Recover
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 86.86 % 85.39 % 87.24 % 88.43 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 89.94 % 98.36 % 93.96 % 34146 571 3819 5.13 % 39085 1664

Benchmark MT PT ML IDS FRAG
CAR 75.38 % 21.69 % 2.92 % 130 521

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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