Method

Graph Neural Network for 2D and 3D Multi-Object Tracking [GNNMOT]
https://github.com/xinshuoweng/GNNMOT

Submitted on 16 Nov. 2019 06:13 by
Xinshuo Weng (Carnegie Mellon University)

Running time:0.01 s
Environment:4 cores @ 2.5 Ghz (Python)

Method Description:
Graph Neural Network for 2D and 3D Multi-Object
Tracking
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 82.24 % 84.05 % 82.66 % 87.43 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 86.08 % 97.88 % 91.60 % 32528 706 5259 6.35 % 36089 1046

Benchmark MT PT ML IDS FRAG
CAR 64.92 % 29.08 % 6.00 % 142 416

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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