Method

Younger3DTracker [Y3D]
https://github.com/yangyingchun1999/Young3DMOT

Submitted on 8 Mar. 2023 14:01 by
yang yingchun (chongqing university)

Running time:0.03 s
Environment:8 cores @ 2.5 Ghz (Python)

Method Description:
an improved Kalman Filter based Lidar
Parameters:
kalman
state_func_covariance=100
measure_func_covariance=0.001
prediction_score_decay=0.02
LiDAR_scanning_frequency=10
Latex Bibtex:
Improved Kalman filter-based 3D multi-target tracking,\inproceedings{IEEE sensors journal}

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 86.48 % 86.77 % 86.89 % 89.60 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 90.97 % 96.92 % 93.85 % 34412 1093 3417 9.83 % 39784 1334

Benchmark MT PT ML IDS FRAG
CAR 76.46 % 20.62 % 2.92 % 139 692

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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