融合模糊统计纹理特征的多线索粒子滤波跟踪

doi:10.13229/j.cnki.jdxbgxb20190789

Abstract

Abstract:

In order to solve the problem that particle filter tracking algorithm uses single feature with lower robustness and the resample strategy is easy to cause particle degradation and impoverishment， an improved particle filter tracking method based on multi-feature and multi-cue is proposed. First， the Histon histogram that introduces the neighborhood relationship is used to describe the color characteristics of the target. Then a robust fuzzy statistical texture feature is used to express spatial texture information， and it is adaptively fused with regional color features to construct a multi-cue observation model. In the particle filter tracking process， K-means-based particle weight clustering is used for more accurate posterior distribution estimation. In the importance resample stage， high-weight particles are retained while new particles are generated based on the prior distribution of the current target state， which ensures particle diversity and avoids particle degradation. Experiments are carried out on the standard test set. Compared with other tracking algorithms based on particle filter framework， the proposed method obtains higher tracking accuracy and success rate. Compared with other popular tracking algorithms， the proposed method can achieve better tracking results under illumination variation， target deformation and background disturbance scenarios.

Key words: information processing technology, particle filter tracking, Histon histogram, fuzzy statistical texture feature, multi-cue

CLC Number:

TP911.72

Jing JIN,Jian-wu DANG,Yang-ping WANG,Dong SHEN. Multi⁃cue particle filter tracking based on fuzzy statistical texture features[J].Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 1111-1120.

Figures/Tables 10

Table 1

Comparison of correct classification percentages"

GLCM			GLRLM			FTV
能量	相关性		长行程因子	行程总数百分比		模糊长行程因子 $R F 1$	模糊行程总数百分比 $R F 2$
82.3	86.6		87.5	89.5		89.8	92.3

Table 1

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算法	IV	OCC	SV	DEF	MB	FM	IPR	OPR	OV	BC	LR	平均值
MSR	0.421	0.614	0.437	0.554	0.498	0.501	0.497	0.399	0.401	0.510	0.482	0.483
LPFT	0.454	0.432	0.659	0.568	0.565	0.492	0.508	0.453	0.385	0.442	0.351	0.482
Chaotic_PF	0.489	0.610	0.475	0.498	0.510	0.604	0.378	0.405	0.562	0.553	0.387	0.497
本文	0.568	0.431	0.465	0.671	0.542	0.548	0.540	0.451	0.398	0.652	0.451	0.519

算法	IV	OCC	SV	DEF	MB	FM	IPR	OPR	OV	BC	LR	平均值
MSR	0.511	0.693	0.604	0.644	0.588	0.614	0.541	0.426	0.540	0.600	0.581	0.577
LPFT	0.572	0.520	0.729	0.674	0.608	0.558	0.609	0.557	0.539	0.574	0.422	0.578
Chaotic_PF	0.504	0.677	0.531	0.510	0.611	0.697	0.415	0.528	0.651	0.559	0.457	0.558
本文	0.664	0.525	0.522	0.706	0.601	0.602	0.633	0.509	0.550	0.709	0.503	0.594

Multi⁃cue particle filter tracking based on fuzzy statistical texture features

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