Abstract
Discriminative correlation filter-based algorithms have recently demonstrated prominent advantages in the community of computer visual tracking, due to their ability to convert ridge regression problems in the frequency domain for creating solutions efficiently, which has attracted a great deal of attention and spurred new research. High precision and robustness have always been the goals of visual tracking. However, during the tracking process, target objects often encounter sophisticated scenarios such as fast motion and occlusion. During this period, erroneous tracking information will be generated and delivered to the next frame for updating; the information will seriously deteriorate the overall tracking model. To address the problem mentioned above, in this paper, we propose an accurate model self-adaptive update method based on a discriminative correlation filter framework. The proposed tracking method is achieved by utilizing the peak score of a response map generated by the discriminative correlation filter as a dynamic threshold with comparisons to its PSR (peak side-lobe ratio) scores, and then the comparative results are used as the differentiated condition for updating the translation filter and scale filter model. In addition, multiple hand-crafted features such as HOG (histogram of oriented gradient), CN (color names), and HOI (histogram of local intensities) are fused self-adaptively for comprehensive feature representation, which further improve tracking performance. We evaluate the performance of the proposed tracker on OTB benchmark datasets; the experimental results demonstrate that the proposed tracker performs favorably against most state-of-the-art discriminative correlation filter-based trackers including some methods follow deep learning paradigm, and the effectiveness of updating the model self-adaptive is verified.










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References
Babenko B, Yang MH, Belongie S (2011) Robust Object Tracking with Online Multiple Instance Learning. IEEE Trans Pattern Anal Mach Intell 33(8):1619–1632
Bertinetto L, Valmadre J, Golodetz S, Miksik O, Torr PHS (2016) Staple: Complementary learners for real-time tracking. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit (CVPR):1401–1409
Bertinetto L, Valmadre J, Henriques JF, Vedaldi A, Torr PHS (2016) Fully-Convolutional Siamese Networks for Object Tracking. Proc Eur Conf Comput Vis (ECCV):850–865
Bolme DS, Beveridge JR, Draper BA, Lui YM (2010) Visual object tracking using adaptive correlation filters. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit (CVPR):2544–2550
Cauwenberghs G, Poggio T (2012) Incremental and decremental support vector machine learning. Proc Annu Conf Neural Inf process Syst:702–715
Cui J, Liu Y, Xu Y et al (2013) Tracking Generic Human Motion via Fusion of Low- and High-Dimensional Approaches. IEEE Transactions on Systems, Man, and Cybernetics: Systems 43(4):996–1002
Danelljan M, Hager G, Khan FS (2015) Learning Spatially Regularized Correlation Filters for Visual Tracking. Proc Eur Conf Comput Vis (ECCV):4310–4318
Danelljan M, Häger G, Khan F, Felsberg M (2014) Accurate scale estimation for robust visual tracking. Proc Brit Mach Vis Conf:65.1–65.11
Danelljan M, Häger G, Khan Shahbaz FS, M F (2015) Convolutional Features for Correlation Filter Based Visual Tracking. Proc IEEE Int Conf Comput Vis (ICCV):621–629
Danelljan M, Khan FS, Felsberg M, van de Weijer J (2014) Adaptive color attributes for real-time visual tracking. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit (CVPR):1090–1097
Danelljan M, Robinson A, Khan FS, Felsberg M (2016) Beyond correlation filters: Learning continuous convolution operators for visual tracking. Proc IEEE Int Conf Comput Vis (ICCV):472–488
Fan H, Ling H (2017) Parallel Tracking and Verifying: A Framework for Real-Time and High Accuracy Visual Tracking. Proc IEEE Int Conf Comput Vis (ICCV):5487–5495
Fan H, Xiang J (2017) Robust Visual Tracking With Multitask Joint Dictionary Learning. IEEE Trans Pattern Ciru Syst Mach Intell 27(3):1018–1030
Felzenszwalb PF, Girshick RB, McAllester DA, Ramanan D (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627–1645
Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking
Gilbert AL, Giles MK, Flachs GM et al (1980) A real-time video tracking system. IEEE Trans Pattern Anal Mach Intell 2(1):47
Grabner H, Leistner C, Bischof H (2008) Semi-supervised On-Line Boosting for Robust Tracking. Proc Eur Conf Comput Vis:234–247
Hare S, Saffari A, Torr PHS (2011) Struck: Structured output tracking with kernels. Proc IEEE Int Conf Comput Vis:263–270
Henriques JF, Caseiro R, Martins P, Batista J (2012) Exploiting the circulant structure of tracking-by-detection with kernels. Proc Eur Conf Comput Vis (ECCV):702–715
Henriques JF, Caseiro R, Martins P, Batista J (2015) High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell 37(3):583–596
Hong S et al (2015) Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network. Proc IEEE Int Conf Mach Learn (ICML):597–606
Kalal Z, Mikolajczyk K, Matas J (2012) Tracking-Learning-Detection. IEEE Trans Pattern Anal Mach Intell 34(7):1409–1422
Kristan M et al (2016) The Visual Object Tracking VOT2015 Challenge Results. Proc IEEE Int Conf Comput Vis (ICCV):564–586
Li H, Li Y, Porikli F (2016) DeepTrack: Learning discriminative feature representations online for robust visual tracking. IEEE Trans Image Process 25(4):1834–1848
Li Z, Tang J (2015) Weakly Supervised Deep Metric Learning for Community-Contributed Image Retrieval. IEEE Transactions on Multimedia 17(11):1989–1999
Li Z, Tang J, He X (2018) Robust Structured Nonnegative Matrix Factorization for Image Representation. IEEE Transactions on Neural Networks and Learning Systems 29(5):1947–1960
Li Y, Zhu J (2014) A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration. Proc Eur Conf Comput Vis (ECCV):254–265
Liu P, Guo JM, Chamnongthai K et al (2017) Fusion of color histogram and LBP-based features for texture image retrieval and classification. Inf Sci 390:95–111
Liu P, Guo JM, Wu CY et al (2017) Fusion of Deep Learning and Compressed Domain Features for Content-Based Image Retrieval. IEEE Trans Image Process PP:99):1–99):1
Ma C, Huang JB, Yang X, Yang MH (2015) Hierarchical Convolutional Features for Visual Tracking. Proc IEEE Int Conf Comput Vis (ICCV):3074–3082
Ma C, Yang X, Zhang C, Yang M-H (2015) Long-term correlation tracking. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit (CVPR):5388–5396
Mei X, Ling H (2009) Robust visual tracking using ℓ 1 minimization. Proc IEEE Conf Compute Soc Vis Pattern Recognit (CVPR):1436–1443
Mueller M, Smith N, Ghanem B (2017) Context-aware correlation filter tracking. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit (CVPR):1396–1404
Nam H, Han B (2016) Learning Multi-domain Convolutional Neural Networks for Visual Tracking. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit (CVPR):4293–4302
Ning J, Jifeng JY, Jiang S, Zhang L, Yang MH (2016) Object Tracking via Dual Linear Structured SVM and Explicit Feature Map. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit (CVPR):4266–4274
Smeulders AWM, Cucchiara DMCR, Calderara S, Dehghan A, Shah M (2014) Visual Tracking: An Experimental Survey. IEEE Trans Pattern Anal Mach Intell 36(37):1442–1468
Tu Z (2005) Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering. IEEE Int Conf Comput Vis:1589–1596
Wang M, Liu Y, Huang Z (2017) Large Margin Object Tracking with Circulant Feature Maps. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit (CVPR):4800–4808
Wang L, Ouyang W, Wang X, Lu H (2015) Visual Tracking with Fully Convolutional Networks. Proc IEEE Int Conf Comput Vis (ICCV):3119–3127
Wang N, Yeung D-Y (2013) Learning a deep compact image representation for visual tracking. Proc Adv Neural Inf Process Syst:809–817
Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemo Intell labora Syst 2(1):37–52
Wu Y, Lim J, Yang MH (2013) Online Object Tracking: A Benchmark. Proc IEEE Conf Comput Vis Pattern Recognit (CVPR):2411–2418
Wu Y, Lim J, Yang MH (2015) Object tracking benchmark. IEEE Trans Pattern Anal Mach Intell 37(9):1834–1848
Yibing S, Ma C, Lijun G, Jiawei Z, Rynson WHL, Ming-Hsuan Y (2017) CREST: Convolutional Residual Learning for Visual Tracking. Proc IEEE Int Conf Comput Vis (ICCV):2574–2583
Yilmaz A, Javed O, Shah M (2006) Object tracking: A survey. ACM Comput Surv 38(4):81–93
Zechao L, Jinhui T, Tao M Deep Collaborative Embedding for Social Image Understanding. IEEE Trans Pattern Anal Mach Intell 2018:1–1
Zhang T, Ghanem B, Liu S, Ahuja N (2012) Robust visual tracking via multi-task sparse learning. Proc IEEE Conf Comput Vis Pattern Recognit (CVPR):2042–2049
Zhang K, Zhang L, Yang M-H (2012) Real-time compressive tracking. Proc Eur Conf Comput Vis:864–877
Zhu Z, Huang G, Zou W, Du D, Huang C (2017) UCT: Learning Unified Convolutional Networks for Real-Time Visual Tracking. Proc IEEE Int Conf Comput Vis (ICCV):1973–1982
Acknowledgments
This work was supported by the grants from National Natural Science Foundation, China under Grant 61605048, and Grant 61603144, and Grant 61403245 and Grant 91648119, in part by Natural Science Foundation of Fujian Province, China under Grant 2015 J01256, and Grant 2016 J01300, in part by the Talent project of Huaqiao University under Grant 14BS215, in part by Quanzhou scientific and technological planning projects of Fujian, China under Grant 2015Z120 and Grant 2017G024, and in part by the Subsidized Project for Postgraduates ‘Innovative Fund in Scientific Research of Huaqiao University under Grant 1611422001.
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Chen, Z., Liu, P., Du, Y. et al. Robust visual tracking using self-adaptive strategy. Multimed Tools Appl 79, 141–162 (2020). https://doi.org/10.1007/s11042-019-08069-z
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DOI: https://doi.org/10.1007/s11042-019-08069-z