Robust tracking via weighted online extreme learning machine (original) (raw)
Adam A, Rivlin E, Shimshoni I (2006) Robust fragments-based tracking using the integral histogram. In: IEEE computer society conference on computer vision and pattern recognition. IEEE Computer Society, pp 798-805
Avidan S (2004) Support vector tracking[J]. IEEE Trans Pattern Anal Mach Intell 26(8):1064–1072 Article Google Scholar
Babenko B, Yang M-H, Belongie S (2011) Robust object tracking with online multiple instance learning[J]. IEEE Trans Pattern Anal Mach Intell 33(8):1619–1632 Article Google Scholar
Bala A, Kaur T (2016) Local texton XOR patterns: a new feature descriptor for content-based image retrieval[J]. Eng Sci Technol Int J 19(1):101–112 Article Google Scholar
Bao C, Wu Y, Ling H, Ji H (2012) Real time robust L1 tracker using accelerated proximal gradient approach[C]. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 1830–1837
Candes EJ, Tao T (2006) Near-optimal signal recovery from random projections: universal encoding strategies?[J]. IEEE Trans Inf Theory 52(12):5406–5425 ArticleMathSciNet Google Scholar
Chi JN, Qian C, Zhang P et al (2014) A novel ELM based adaptive Kalman filter tracking algorithm[J]. Neurocomputing 128(5):42–49 Article Google Scholar
Chrysos GG, Antonakos E, Zafeiriou S et al (2015) Offline deformable face tracking in arbitrary videos[C]. In: Proceedings of the IEEE international conference on computer vision workshops, pp 1–9
Collins R, Liu Y, Leordeanu M (2005) Onlineselectionofdiscriminative tracking features[C]. IEEE Trans Pattern Anal Mach Intell 27(10):1631–1643 Article Google Scholar
Deng CW, Huang GB, Xu J et al (2015) Extreme learning machines: new trends and applications[J]. Sci China Inf Sci 58(2):1–16 Article Google Scholar
Elkan C (2001) The foundations of cost-sensitive learning[C]. Int Joint Conf Artif Intell, Lawrence Erlbaum Assoc Ltd 17(1):973–978 Google Scholar
Everingham M, Van Gool L, Williams CKI et al (2010) The pascal visual object classes (voc) challenge[J]. Int J Comput Vis 88(2):303–338 Article Google Scholar
Exner D, Bruns E, Kurz D et al (2010) Fast and robust CAMShift traeking [C]. In: IEEE computer society conference on computer vision and pattern recognition, pp 9–16
Grabner H, Grabner M, Bischof H (2006) Real-time tracking via on-line boosting[C]. Bmvc 1(5):6 Google Scholar
Grabner H, Grabner M, Bischof H (2006) Real-time tracking via online boosting[C]. In: Proceedings of British machine vision conference, pp 47–56
He X et al (2014) Networked strong tracking filtering with multiple packet dropouts: algorithms and applications[J]. IEEE Trans Ind Electron 61(3):1454–1463 Article Google Scholar
Huang G, Huang GB, Song S et al (2015) Trends in extreme learning machines: a review[J]. Neural Netw 61:32–48 Article Google Scholar
Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications[J]. Neurocomputing 70(1):489–501 Article Google Scholar
Jia X, Lu H, Yang MH (2012) Visual tracking via adaptive structural local sparse appearance model[C]. CVPR, pp 1822–1829
Kalal Z, Matas J, Mikolajczyk K (2010) Pn learning: bootstrapping binary classifiers by structural constraints[C]. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 49–56
Kasun LLC, Yang Y, Huang GB et al (2016) Dimension reduction with extreme learning machine[J]. IEEE Trans Image Process 25(8):3906–3918 ArticleMathSciNet Google Scholar
Li H, Shen C, Shi Q (2011) Real-time visual tracking using compressive sensing[C]. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 1305–1312
Li X, Dick A, Wang H et al (2011) Graph mode-based contextual kernels for robust SVM tracking[C]. In: 2011 IEEE international conference on computer vision (ICCV). IEEE, pp 1156–1163
Li H., Li Y, Porikli F (2014) Robust online visual tracking with a single convolutional neural network[C]. In: Proceedings of 12th Asian conference computer vision, pp 194–209 Chapter Google Scholar
Liu I, Sun Y (1993) Recursive tracking of vascular networks in angiograms based on the detection-deletion scheme[J]. IEEE Trans Med Imaging 12(2):334–341 Article Google Scholar
Mei X, Ling H, Wu Y, Blasch E, Bai L (2011) Minimum error bounded efficient LI tracker with occlusion detection[C]. In: IEEE conference on computer vision and pattern recognition, CVPR 2011, Colorado Springs, Co, Usa, 20-25 June. DBLP, pp 1257–1264
Parag T, Porikli F, Elgammal A (2008) Boosting adaptive linear weak classifiers for online learning and tracking[C]. In: IEEE conference on computer vision and pattern recognition, 2008. CVPR 2008. IEEE, pp 1–8
Revaud J et al (2015) Epicflow: edge-preserving interpolation of correspondences for optical flow[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition
Rong HJ, Huang GB, Sundararajan N et al (2009) Online sequential fuzzy extreme learning machine for function approximation and classification problems[J]. IEEE Trans Syst Man Cybern Part B (Cybernetics) 39(4):1067–1072 Article Google Scholar
Ross DA, Lim J, Lin RS et al (2008) Incremental learning for robust visual tracking[J]. Int J Comput Vis 77(1):125–141 Article Google Scholar
Schindelin J et al (2015) The ImageJ ecosystem: an open platform for biomedical image analysis[J]. Mol Reprod Dev 82(7-8):518–529 Article Google Scholar
Tang J, Deng C, Huang GB (2016) Extreme learning machine for multilayer perceptron[J]. IEEE Trans Neural Netw Learn Syst 27(4):809–821 ArticleMathSciNet Google Scholar
Wang S, Lu H, Yang F, Yang M-H (2011) Uperpixel tracking[C]. In: Proceedings of the IEEE international conference on computer vision, pp 1323–1330
Wang Y, Lin X, Wu L, Zhang W (2015) Effective multi-query expansions: Robust Landmark retrieval[J]. In: ACM multimedia. ACM
Wang Y, Lin X, Wu L, Zhang W, Zhang Q (2015) LBMCH: learning bridging mapping for cross-modal hashing[J]. ACM SIGIR
Wang Y, Lin X, Wu L, Zhang W, Zhang Q, Huang X (2015) Robust subspace clustering for multi-view data by exploiting correlation consensus[J]. IEEE Trans Image Process 24(11):3939–3949 ArticleMathSciNet Google Scholar
Wang Y, Zhang W, Wu L, Lin X, Fang M, Pan S (2016) Iterative views agreement: an iterative low-rank based structured optimization method to multi-view spectral clustering[J]. arXiv:1608.05560
Wang Y, Zhang W, Wu L, Lin X, Zhao X (2017) Unsupervised metric fusion over multiview data by graph random walk-based cross-view diffusion[J]. IEEE Trans Neural Netw Learn Syst 28(1):57–70 Article Google Scholar
Wang Y, Lin X, Wu L, Zhang W (2017) Effective multi-query expansions: collborative deep networks for Robust Landmark retrieval[J]. IEEE Trans Image Process 26(3):1393–1404 ArticleMathSciNet Google Scholar
Wang Y, Wu L (2018) Beyond low-rank representations: Orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering[J]. Neural Netw 103:1–8 Article Google Scholar
Wang Y, Wu L, Lin X, Gao J (2018) Multiview spectral clustering via structured low-rank matrix factorization[J]. IEEE Transactions on Neural Networks and Learning Systems
Wen J, Gao X, Yuan Y, Tao D, Li J (2010) Incremental tensor biased discriminant analysis: a new color-based visual tracking method[J]. Neurocomputing 73 (4-6):827–839 Article Google Scholar
Wu L, Wang Y, Shepherd J (2013) Efficient image and tag co-ranking: a Bregman divergence optimization method[J]. ACM Multimedia
Wu L, Wang Y (2017) Robust hashing for multi-view data: jointly learning low-rank kernelized similarity consensus and Hash functions[J]. Image Vis Comput 57:58–66 Article Google Scholar
Wu L, Wang Y, Gao J, Li X (2017) Deep adaptive feature embedding with local sample distributions for person re-identification[J]. Pattern Recogn 73:275–288 Article Google Scholar
Wu L, Wang Y, Ge Z, Hu Q, Li X (2018) Structured deep hashing with convolutional neural networks for fast person re-identification[J]. Comput Vis Image Underst 167:63–73 Article Google Scholar
Wu L, Wang Y, Li X, Gao J (2018) Deep attention-based spatially recursive networks for fine-grained visual recognition[J]. IEEE Transactions on Cybernetics
Wu L, Wang Y, Li X, Gao J (2018) What-and-Where to match: deep spatially multiplicative integration networks for person re-identification[J]. Pattern Recogn 76:727–738 Article Google Scholar
Wu L, Wang Y, Shao L (2018) Cycle-consistent deep generative hashing for cross-modal retrieval[J]. arXiv:1804.11013
Yeh YJ, Hsu CT (2009) Online selection of tracking features using AdaBoost[J]. IEEE Trans Circ Syst Video Technol 19(3):442–446 Article Google Scholar
Zhang K, Zhang L, Yang M-H (2012) Real-time compressive tracking. In: European conference on computer vision[C]. Springer, Berlin Heidelberg Book Google Scholar
Zhang K, Zhang L, Liu Q et al (2014) Fast visual tracking via dense spatio-temporal context learning[C]. In: European conference on computer vision. Springer International Publishing, pp 127–141
Zhang K, Zhang L, Yang MH (2014) Fast compressive tracking[J]. IEEE Trans Pattern Anal Mach Intell 36(10):2002–2015 Article Google Scholar
Zhang J, Feng L, Yu L (2016) A novel target tracking method based on OSELM[J]. Multidim Syst Sign Process 28(3):1–18 Google Scholar
Zhou X, Xie L, Zhang P, Zhang Y (2015) An ensemble of deep neural networks for object tracking[C]. In: IEEE international conference on image processing. IEEE, pp 843–847
Zong W, Huang GB, Chen Y (2013) Weighted extreme learning machine for imbalance learning[J]. Neurocomputing 101:229–242 Article Google Scholar