Method

Multi-target Tracking Algorithm Based on Fusion and NC2 Noise Covariance Estimation [la][on] [FNC2]


Submitted on 28 Jun. 2021 09:44 by
Jiang Chao (University of science and technology of China)

Running time:0.01 s
Environment:1 core @ 3.0 Ghz (C/C++)

Method Description:
Laser radar data is prone to false detection
because of its simple features. To solve this
problem, we propose a fast image-based re-
classification algorithm. Each detection result of
laser radar is classified and recognized by the re-
classification algorithm, so as to reduce the false
detection. Then,the detection results are fed into
our proposed NC2 adaptive filter and multi-target
tracker for multi-target tracking.
Parameters:
python3.7
Latex Bibtex:
@InProceedings{Jiang2024Adaptive,
author = {Chao Jiang and Zhiling Wang and Huawei
Liang and Yajun Wang},
title = {A Novel Adaptive Noise Covariance
Matrix Estimation and Filtering Method:
Application to Multiobject Tracking},
booktitle = {IEEE Transactions on Intelligent
Vehicles},
year = {2024},
pages = {626-641},
month = jan,
}

@Article{Jiang2022Object,
author = {Chao Jiang and Zhiling Wang and Huawei
Liang},
title = {A Fast and High-Performance Object
Proposal Method for Vision Sensors: Application to
Object Detection},
journal = {IEEE Sensors Journal},
year = {2022},
month = jan,
}

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 84.75 % 85.80 % 84.84 % 88.79 %
PEDESTRIAN 56.52 % 66.07 % 58.03 % 88.75 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 93.03 % 93.72 % 93.37 % 36726 2461 2752 22.12 % 45817 1598
PEDESTRIAN 74.12 % 82.69 % 78.17 % 17397 3642 6074 32.74 % 28745 1262

Benchmark MT PT ML IDS FRAG
CAR 76.00 % 18.15 % 5.85 % 33 311
PEDESTRIAN 43.99 % 43.64 % 12.37 % 349 1492

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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