Method

3D Multi-Object Tracking by Fusion of LiDAR and Image Detections [MOT_FLID]
[Anonymous Submission]

Submitted on 19 Apr. 2020 21:47 by
[Anonymous Submission]

Running time:0.05 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
TBD
Parameters:
TBD
Latex Bibtex:
TBD

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 83.03 % 85.21 % 83.09 % 88.17 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 89.04 % 95.20 % 92.02 % 33518 1690 4127 15.19 % 37735 777

Benchmark MT PT ML IDS FRAG
CAR 69.08 % 19.23 % 11.69 % 20 151

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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