An Efficient Tracker via Multi-feature Adaptive Correlation Filter

doi:10.16182/j.issn1004731x.joss.21-0274

Abstract

Abstract:

Aiming at the low tracking effect of the correlation filters tracker based on manual features in challenging scenes of rapid deformation and background clutter, a new correlation filter tracker based on Staple tracker is proposed. An appearance model based on HOG features and color-naming features is built to enhance the robustness to the challenging scenes of rapid deformation and background clutter. A self-adjust evaluation function is designed to merge the two kinds of feature information and a more discriminative feature is obtained. The novel online update strategies to reduce the training over-fitting and model drift for different features are proposed. The tracker shows excellent performance in accuracy and real-time capability on OTB2015 benchmark.

Key words: object tracking, correlation filter, self-adjust evaluation function, color-naming feature, online update

CLC Number:

TP391.4

Sixian Zhang, Yi Yang, Meng Zhang, Pengbo Mi. An Efficient Tracker via Multi-feature Adaptive Correlation Filter[J]. Journal of System Simulation, 2022, 34(8): 1864-1873.

Figures/Tables 7

Fig. 1

Table 1

Experimental Parameters

主要参数	量值	主要参数	量值
学习率 $η$	0.01	融合参数 $α 2$	0.6
颜色特征	Color Naming	检测区域	150×150
融合参数 $α 1$	0.3	Hog单元尺寸	4×4

Table 1

Fig. 2

Table 2

Fig. 3

Fig. 4

Table 3

References 26

1	Bolme D S, Ross Beveridge J, Draper Bruce A, et al. Visual Object Tracking Using Adaptive Correlation Filters[C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, CA: IEEE, 2010: 2544-2550.
2	Zhang T, Liu S, Xu C, et al. Correlation Particle Filter for Visual Tracking[J]. IEEE Transactions on Image Processing(S1057-7149), 2018, 27(6): 2676-2687.
3	Zhang T, Xu C, Yang M. Learning Multi-Task Correlation Particle Filters for Visual Tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence(S0162-8828), 2019, 41(2): 365-378.
4	Li S, Zhao S, Cheng B, et al. Robust Visual Tracking Via Hierarchical Particle Filter and Ensemble Deep Features[J]. IEEE Transactions on Circuits and Systems for Video Technology(S1051-8215), 2020, 30(1): 179-191.
5	Henriques J, Caseiro R, Martins P, et al. Exploiting the Circulant Structure of Tracking-by-Detection with Kernels[C]//12th European Conference on Computer Vision. Berlin, Heidelberg: Springer, 2012: 702-715.
6	Danelljan M, Häger G, Khan F S, et al. Learning Spatially Regularized Correlation Filters for Visual Tracking[C]//2015 IEEE International Conference on Computer Vision. Santiago: IEEE, 2015: 4310-4318.
7	Song Z, Sun J, Duan B. Collaborative Correlation Filter Tracking with Online Re-detection[C]//2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). Chengdu: IEEE, 2019: 1303-1313.
8	Zhang S, Lu W, Xing W, et al. Learning Scale-Adaptive Tight Correlation Filter for Object Tracking[J]. IEEE Transactions on Cybernetics(S2168-2267), 2020, 50(1): 270-283.
9	Henriques J F, Caseiro R, Martins P, et al. High-Speed Tracking with Kernelized Correlation Filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (S0162-8828), 2015, 37(3): 583-596.
10	Lukežič A, Čehovin Zajc L, Kristan M. Deformable Parts Correlation Filters for Robust Visual Tracking[J]. IEEE Transactions on Cybernetics(S2168-2267), 2018, 48(6): 1849-1861.
11	Martin Danelljan, Gustav Häger, Khan Shahbaz Fahad, et al. Discriminative Scale Space Tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (S0162-8828), 2017, 39(8): 1561-1575.
12	Danelljan M, Khan F S, Felsberg M, et al. Adaptive Color Attributes for Real-Time Visual Tracking[C]//2014 IEEE Conference on Computer Vision and Pattern Recogni-tion. Columbus, OH: IEEE, 2014: 1090-1097.
13	Gundogdu E, Alatan A A. Good Features to Correlate for Visual Tracking[J]. IEEE Transactions on Image Processing(S1057-7149), 2018, 27(5): 2526-2540.
14	Bertinetto L, Valmadre J, Golodetz S, et al. Staple: Complementary Learners for Real-Time Tracking[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV: IEEE, 2016: 1401-1409.
15	Valmadre J, Bertinetto L, Henriques J, et al. End-to-End Representation Learning for Correlation Filter Based Tracking[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI: IEEE, 2017: 5000-5008.
16	Feng J, Pu S, Zhao K, et al. Enhanced Initialization with Multi-Stage Learning for Robust Visual Tracking[C]//2019 IEEE Visual Communications and Image Processing. Sydney, NSW: IEEE, 2019: 1-4.
17	Bhat G, Danelljan M, Van Gool L, et al. Learning Discriminative Model Prediction for Tracking[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul, Korea(South): IEEE, 2019: 6181-6190.
18	Danelljan M, Bhat G, Khan F S, et al. ATOM: Accurate Tracking by Overlap Maximization[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, CA: IEEE, 2019: 4655-4664.
19	Guo D, Wang J, Cui Y, et al. SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA: IEEE, 2020: 6268-6276.
20	Voigtlaender P, Luiten J, Torr P H S, et al. SiamR-CNN: Visual Tracking by Re-Detection[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA: IEEE, 2020: 6577-6587.
21	Li B, Wu W, Wang Q, et al. SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, CA: IEEE, 2019: 4277-4286.
22	Hu J, Shen L, Albanie S, et al. Squeeze-and-Excitation Networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence(S0162-8828), 2020, 42(8): 2011-2023.
23	Danelljan M, Van Gool L, Timofte R. Probabilistic Regression for Visual Tracking[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA: IEEE, 2020: 7181-7190.
24	Martin Danelljan, Goutam Bhat, Khan Fahad Shahbaz, et al. ECO: Efﬁcient Convolution Operators for Tracking[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI: IEEE, 2017: 6931-6939.
25	Wang M, Liu Y, Huang Z. Large Margin Object Tracking with Circulant Feature Maps [C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI: IEEE, 2017: 4021-4029.
26	Wu Y, Lim J, Yang M H. Object Tracking Benchmark[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (S0162-8828), 2015, 37(9): 1834-1848.

跟踪器	帧数	跟踪器	帧数
CSK	275.0	ECO_HC	37.5
KCF	120.5	DSST	33.7
Staple	55.1	CN	13.5
STACL	50.3	SRDCF	9.8

challenges	STACL	Staple
变形	0.640	0.617
背景杂波	0.682	0.623
光照变化	0.631	0.596
面外旋转	0.594	0.561
面内旋转	0.589	0.549
尺度变化	0.613	0.552
遮挡	0.593	0.621
抖动	0.631	0.625
快速移动	0.657	0.651
低分辨率	0.570	0.569

[1]	Wei Zhou, Yuxiang Liu, Guangping Liao, Xin Ma. Siamese Object Tracking Algorithm Combined with the Intersection over Union Loss [J]. Journal of System Simulation, 2022, 34(9): 1956-1967.
[2]	Li Qi, Mo Hanlin, Wang Xiangdong, Li Hua. Multiple Object Tracking and Kinematic Simulation for Short Track Speed Skating [J]. Journal of System Simulation, 2021, 33(5): 1039-1050.
[3]	Zhang Liang, Hao Kaifeng. Matching between MAC Address and Object Based on RSSI Change Sequence [J]. Journal of System Simulation, 2020, 32(1): 113-121.
[4]	Li Ju, Cao Mingwei, Yu Ye, XiaYu, Zhou Lifan. An Anti-occlusion Adaptive Particle Filtering Algorithm [J]. Journal of System Simulation, 2018, 30(9): 3552-3557.
[5]	Jiang Mingxin, Pan Zhigeng, WangLanfang, Hu Taoxin. Visual Object Tracking Algorithm Based on Deep Denoising Autoencoder over RGB-D Data [J]. Journal of System Simulation, 2018, 30(11): 4276-4283.
[6]	Yang Jiabo, Yang Gang, Yang Meng. Augmented Reality System Based on Depth Image Segmentation and Object Tracking [J]. Journal of System Simulation, 2017, 29(11): 2788-2795.
[7]	Niu Weihua, Liang Guishu, Zhao Peng. Fault Diagnosis Model of Circuit Breaker Based on Sparse Representation and M-ELM [J]. Journal of System Simulation, 2017, 29(11): 2828-2839.