Method

Multi-target Tracking Algorithm Based on NC2 Adaptive Noise Covariance Estimation [la][on] [NC2]


Submitted on 23 Jun. 2021 13:07 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:
NC2 adaptive filtering and noise estimation method
are proposed to estimate the process and
measurement noise in real time, so as to improve
the stability and accuracy of tracking. NC2 is a
closed-loop estimation method based on estimation,
calibration and correction, which is different
from the existing adaptive covariance estimation
algorithms. First of all, there are less
restrictions on technical assumptions. It is
assumed that the system is asymptotically stable
rather than observable, and there is no
restriction on the dimensions of the state
transition matrix and the observation matrix.
Secondly, when the process and observation noises
are unknown, the noise covariance can also be
estimated.
Parameters:
python=3.7
Latex Bibtex:
@ARTICLE{10154170,
author={Jiang, Chao and Wang, Zhiling and Liang,
Huawei and Wang, Yajun},
journal={IEEE Transactions on Intelligent
Vehicles},
title={A Novel Adaptive Noise Covariance Matrix
Estimation and Filtering Method: Application to
Multiobject Tracking},
year={2024},
volume={9},
number={1},
pages={626-641},
keywords={Covariance matrices;Noise
measurement;Estimation;Correlation;Filtering;Calib
ration;Technological innovation;Kalman
filtering;adaptive estimation;process and
measurement noise covariance matrices;multiobject
tracking},
doi={10.1109/TIV.2023.3286979}}

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 78.95 % 85.82 % 79.04 % 88.76 %
PEDESTRIAN 44.64 % 66.08 % 46.14 % 88.82 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 93.24 % 89.00 % 91.07 % 36770 4543 2665 40.84 % 48752 2133
PEDESTRIAN 74.05 % 73.16 % 73.60 % 17381 6378 6090 57.34 % 32095 1779

Benchmark MT PT ML IDS FRAG
CAR 76.00 % 18.31 % 5.69 % 31 275
PEDESTRIAN 43.99 % 42.96 % 13.06 % 348 1488

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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