Correlation filtering based target tracking with nonlinear temporal consistency

doi:10.11772/j.issn.1001-9081.2023081121

Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (8): 2558-2570.DOI: 10.11772/j.issn.1001-9081.2023081121

• Multimedia computing and computer simulation • Previous Articles

Correlation filtering based target tracking with nonlinear temporal consistency

Wentao JIANG¹, Wanxuan LI¹(), Shengchong ZHANG²

^1.School of Software，Liaoning Technical University，Huludao Liaoning 125105，China
^2.Science and Technology on Electro?Optical Information Security Control Laboratory，Tianjin 300308，China

Received:2023-08-21 Revised:2023-11-07 Accepted:2023-11-15 Online:2023-12-18 Published:2024-08-10
Contact: Wanxuan LI
About author:JIANG Wentao， born in 1986， Ph. D.， associate professor. His research interests include image processing， pattern recognition， deep learning.
ZHANG Shengchong， born in 1973， M. S. ， senior engineer. His research interests include image processing， pattern recognition， video target tracking.
Supported by:
National Defense Preliminary Research Fund(172068);Natural Science Foundation of Liaoning Province(20170540426);Key Fund of Liaoning Provincical Department of Education(LJYL049)

非线性时间一致性的相关滤波目标跟踪

姜文涛¹, 李宛宣¹(), 张晟翀²

^1.辽宁工程技术大学软件学院，辽宁葫芦岛 125105
^2.光电信息控制和安全技术重点实验室，天津 300308

通讯作者: 李宛宣
作者简介:姜文涛（1986—），男，辽宁大连人，副教授，博士，主要研究方向：图像处理、模式识别、深度学习
李宛宣（1999—），女，辽宁铁岭人，硕士研究生，CCF会员，主要研究方向：目标跟踪、模式识别、深度学习 1243291724@qq.com
张晟翀（1973—），男，安徽合肥人，高级工程师，硕士，主要研究方向：图像处理、模式识别、视频目标跟踪。
基金资助:
国防预研基金资助项目(172068);辽宁省自然科学基金资助项目(20170540426);辽宁省教育厅重点基金资助项目(LJYL049)

Abstract

Abstract:

Concerning the problem that existing target tracking algorithms mainly use the linear constraint mechanism LADCF （Learning Adaptive Discriminative Correlation Filters）， which easily causes model drift， a correlation filtering based target tracking algorithm with nonlinear temporal consistency was proposed. First， a nonlinear temporal consistency term was proposed based on Stevens’ Law， which aligned closely with the characteristics of human visual perception. The nonlinear temporal consistency term allowed the model to track the target relatively smoothly， thus ensuring tracking continuity and preventing model drift. Next， the Alternating Direction Method of Multipliers （ADMM） was employed to compute the optimal function value， ensuring real-time tracking of the algorithm. Lastly， Stevens’ Law was used for nonlinear filter updating， enabling the filter update factor to enhance and suppress the filter according to the change of the target， thereby adapting to target changes and preventing filter degradation. Comparison experiments with mainstream correlation filtering and deep learning algorithms were performed on four standard datasets. Compared with the baseline algorithm LADCF， the tracking precision and success rate of the proposed algorithm were improved by 2.4 and 3.8 percentage points on OTB100 dataset， and 1.5 and 2.5 percentage points on UAV123 dataset. The experimental results show that the proposed algorithm effectively avoids tracking model drift， reduces the likelihood of filter degradation， has higher tracking precision and success rate， and stronger robustness in complicated situations such as occlusion and illumination changes.

Key words: target tracking, correlation filtering, Stevens’ Law, nonlinear temporal consistency, nonlinear filter updating

摘要：

针对现有目标跟踪算法主要采用线性约束机制LADCF（Learning Adaptive Discriminative Correlation Filters）跟踪模型容易漂移的问题，提出非线性时间一致性的相关滤波目标跟踪算法。首先，结合史蒂文斯定律，提出贴近人类视觉感知特性的非线性时间一致项，使模型相对平滑地跟踪目标，从而保证跟踪连续性，避免跟踪模型漂移；其次，采用交替方向乘子法（ADMM）求解最优函数值，保证算法的跟踪实时性；最后，利用史蒂文斯定律非线性更新滤波器，使滤波器更新因子可以根据目标的变化增强和抑制滤波器，以适应目标变化，防止滤波器退化。在4个标准数据集上与主流相关滤波和深度学习算法对比实验，相较于基线算法LADCF，所提算法的跟踪精确度和成功率在OTB100数据集上分别提升了2.4和3.8个百分点；在UAV123上分别提升了1.5和2.5个百分点。实验结果表明，所提算法能有效避免跟踪模型漂移，降低滤波器退化概率，跟踪精确度和成功率较高，面对遮挡、光照变化等复杂场景时具有较强的鲁棒性。

CLC Number:

TP391.4

Wentao JIANG, Wanxuan LI, Shengchong ZHANG. Correlation filtering based target tracking with nonlinear temporal consistency[J]. Journal of Computer Applications, 2024, 44(8): 2558-2570.

姜文涛, 李宛宣, 张晟翀. 非线性时间一致性的相关滤波目标跟踪[J]. 《计算机应用》唯一官方网站, 2024, 44(8): 2558-2570.

Figures/Tables 21

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算法	OTB50数据集		OTB100数据集		UAV123数据集		DTB70数据集
算法	DP	AUC	DP	AUC	DP	AUC	DP	AUC
本文算法	0.830	0.796	0.838	0.798	0.711	0.596	0.689	0.482
STRCF算法	0.830	0.788	0.829	0.769	0.681	0.563	0.670	0.472
LADCF算法	0.818	0.771	0.814	0.760	0.696	0.571	0.660	0.454
EMCF算法	0.803	0.739	0.774	0.711	0.697	0.574	0.719	0.496
BACF算法	0.800	0.755	0.786	0.738	0.664	0.555	0.582	0.398
ACSDCF算法	0.798	0.737	0.799	0.761	0.719	0.591	0.649	0.437
AutoTrack算法	0.744	0.707	0.765	0.688	0.688	0.562	0.717	0.479
ARCF算法	0.759	0.712	0.773	0.700	0.660	0.557	0.629	0.428
SRDCF算法	0.724	0.680	0.734	0.672	0.676	0.551	0.512	0.363

算法	OTB50数据集		OTB100数据集		UAV123数据集		DTB70数据集
算法	DP	AUC	DP	AUC	DP	AUC	DP	AUC
本文算法	0.830	0.796	0.838	0.798	0.711	0.596	0.689	0.482
STRCF算法	0.830	0.788	0.829	0.769	0.681	0.563	0.670	0.472
LADCF算法	0.818	0.771	0.814	0.760	0.696	0.571	0.660	0.454
EMCF算法	0.803	0.739	0.774	0.711	0.697	0.574	0.719	0.496
BACF算法	0.800	0.755	0.786	0.738	0.664	0.555	0.582	0.398
ACSDCF算法	0.798	0.737	0.799	0.761	0.719	0.591	0.649	0.437
AutoTrack算法	0.744	0.707	0.765	0.688	0.688	0.562	0.717	0.479
ARCF算法	0.759	0.712	0.773	0.700	0.660	0.557	0.629	0.428
SRDCF算法	0.724	0.680	0.734	0.672	0.676	0.551	0.512	0.363

算法	OTB50数据集		OTB100数据集		UAV123数据集（Partail）		UAV123数据集（Full）		DTB70数据集
算法	DP	AUC	DP	AUC	DP	AUC	DP	AUC	DP	AUC
本文算法	0.798	0.752	0.797	0.760	0.645	0.506	0.518	0.321	0.706	0.476
STRCF算法	0.798	0.736	0.763	0.689	0.587	0.468	0.488	0.289	0.599	0.425
LADCF算法	0.794	0.743	0.761	0.707	0.619	0.489	0.481	0.285	0.663	0.434
EMCF算法	0.750	0.675	0.666	0.602	0.614	0.488	0.506	0.301	0.648	0.449
BACF算法	0.756	0.699	0.686	0.632	0.568	0.442	0.436	0.251	0.515	0.347
ACSDCF算法	0.771	0.694	0.756	0.702	0.637	0.505	0.509	0.308	0.617	0.400
AutoTrack算法	0.738	0.678	0.688	0.621	0.610	0.475	0.491	0.288	0.631	0.415
ARCF算法	0.728	0.690	0.704	0.647	0.558	0.458	0.405	0.257	0.688	0.447
SRDCF算法	0.697	0.643	0.650	0.588	0.608	0.468	0.501	0.301	0.478	0.310

算法	OTB50数据集		OTB100数据集		UAV123数据集（Partail）		UAV123数据集（Full）		DTB70数据集
算法	DP	AUC	DP	AUC	DP	AUC	DP	AUC	DP	AUC
本文算法	0.798	0.752	0.797	0.760	0.645	0.506	0.518	0.321	0.706	0.476
STRCF算法	0.798	0.736	0.763	0.689	0.587	0.468	0.488	0.289	0.599	0.425
LADCF算法	0.794	0.743	0.761	0.707	0.619	0.489	0.481	0.285	0.663	0.434
EMCF算法	0.750	0.675	0.666	0.602	0.614	0.488	0.506	0.301	0.648	0.449
BACF算法	0.756	0.699	0.686	0.632	0.568	0.442	0.436	0.251	0.515	0.347
ACSDCF算法	0.771	0.694	0.756	0.702	0.637	0.505	0.509	0.308	0.617	0.400
AutoTrack算法	0.738	0.678	0.688	0.621	0.610	0.475	0.491	0.288	0.631	0.415
ARCF算法	0.728	0.690	0.704	0.647	0.558	0.458	0.405	0.257	0.688	0.447
SRDCF算法	0.697	0.643	0.650	0.588	0.608	0.468	0.501	0.301	0.478	0.310

算法	IV	SV	OCC	DEF	MB	FM	IPR	OPR	OV	BC	LR
本文算法	0.779	0.813	0.797	0.786	0.758	0.771	0.816	0.835	0.778	0.762	0.719
LADCF算法	0.762	0.793	0.761	0.753	0.740	0.738	0.788	0.800	0.736	0.742	0.661
SRDCF算法	0.729	0.691	0.650	0.696	0.648	0.651	0.731	0.685	0.605	0.677	0.538
BACF算法	0.754	0.748	0.686	0.752	0.667	0.748	0.755	0.746	0.684	0.727	0.669
STRCF算法	0.780	0.804	0.763	0.788	0.765	0.752	0.789	0.797	0.717	0.798	0.672
ARCF算法	0.715	0.757	0.704	0.702	0.725	0.715	0.715	0.723	0.738	0.693	0.663
AutoTrack算法	0.719	0.731	0.688	0.699	0.688	0.684	0.727	0.721	0.679	0.705	0.671
ACSDCF算法	0.761	0.775	0.756	0.730	0.765	0.769	0.791	0.778	0.667	0.741	0.767
EMCF算法	0.749	0.723	0.666	0.752	0.678	0.720	0.741	0.728	0.582	0.746	0.656

Correlation filtering based target tracking with nonlinear temporal consistency

非线性时间一致性的相关滤波目标跟踪

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Figures/Tables 21

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Metrics

算法	IV	SV	OCC	DEF	MB	FM	IPR	OPR	OV	BC	LR
本文算法	0.762	0.769	0.760	0.740	0.742	0.756	0.755	0.788	0.708	0.733	0.624
LADCF算法	0.728	0.737	0.707	0.684	0.722	0.711	0.708	0.729	0.670	0.700	0.569
SRDCF算法	0.678	0.603	0.588	0.644	0.626	0.634	0.655	0.622	0.528	0.632	0.384
BACF算法	0.730	0.700	0.632	0.690	0.661	0.727	0.689	0.686	0.594	0.692	0.571
STRCF算法	0.742	0.728	0.689	0.704	0.731	0.711	0.715	0.730	0.656	0.760	0.572
ARCF算法	0.677	0.675	0.647	0.596	0.681	0.672	0.613	0.633	0.645	0.645	0.549
AutoTrack算法	0.671	0.646	0.621	0.604	0.646	0.642	0.617	0.620	0.581	0.626	0.551
ACSDCF算法	0.714	0.749	0.702	0.691	0.756	0.765	0.727	0.720	0.682	0.723	0.745
EMCF算法	0.706	0.644	0.602	0.704	0.642	0.677	0.649	0.641	0.540	0.713	0.591

算法	SV	ARC	LR	FM	FOC	POC	OV	BC	IV	VC	CM	SO
本文算法	0.558	0.488	0.415	0.408	0.321	0.509	0.478	0.410	0.477	0.503	0.526	0.547
LADCF算法	0.526	0.438	0.384	0.382	0.285	0.489	0.467	0.376	0.399	0.443	0.556	0.583
SRDCF算法	0.510	0.431	0.359	0.366	0.301	0.468	0.465	0.433	0.457	0.441	0.512	0.532
BACF算法	0.509	0.442	0.335	0.387	0.251	0.442	0.462	0.380	0.411	0.454	0.538	0.546
STRCF算法	0.516	0.418	0.388	0.359	0.289	0.468	0.470	0.382	0.385	0.426	0.545	0.550
ARCF算法	0.512	0.440	0.377	0.349	0.257	0.458	0.441	0.418	0.457	0.429	0.518	0.537
AutoTrack算法	0.515	0.440	0.386	0.380	0.288	0.475	0.455	0.381	0.408	0.447	0.553	0.561
ACSDCF算法	0.548	0.470	0.411	0.417	0.308	0.505	0.506	0.451	0.473	0.473	0.578	0.580
EMCF算法	0.529	0.434	0.392	0.383	0.301	0.488	0.469	0.377	0.400	0.442	0.564	0.591

OSTrack		本文算法		GradNet		DeepSTRCF		TransT
DP	AUC	DP	AUC	DP	AUC	DP	AUC	DP	AUC
0.848	0.823	0.838	0.789	0.837	0.796	0.833	0.797	0.820	0.807
LightTrack		DeepSRDCF		ECO		SiamFC		CFNet
DP	AUC	DP	AUC	DP	AUC	DP	AUC	DP	AUC
0.818	0.765	0.804	0.735	0.787	0.746	0.764	0.736	0.757	0.724

算法	FPS	算法	FPS
本文算法	26.20	ARCF算法	37.30
LADCF算法	24.60	AutoTrack算法	25.20
SRDCF算法	13.13	ACSDCF算法	31.79
BACF算法	51.48	EMCF算法	66.89
STRCF算法	26.18

算法	NTCT	NFU	DP	AUC
算法1	×	×	0.814	0.760
算法2	√	×	0.825	0.767
算法3	×	√	0.820	0.782
算法4	√	√	0.838	0.798

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