A parallel and robust object tracking approach synthesizing adaptive Bayesian learning and improved incremental subspace learning
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Ali A, Jalil A, Niu J, Zhao X, Rathore S, Ahmed J, Iftikhar MA. Visual object tracking-classical and contemporary approaches. Frontiers of Computer Science, 2016, 10(1): 167–188
Wang Y, Zhao Q. Patchwise tracking via spatio-temporal constraintbased sparse representation and multiple-instance learning-based SVM. In: Proceedings of International Conference on Neural Information Processing. 2015, 264–271
Li K, He F, Chen X. Real-time object tracking via compressive feature selection. Frontiers of Computer Science, 2016, 10(4): 689–701
Wu G, Lu W, Gao G, Zhao C, Liu J. Regional deep learning model for visual tracking. Neurocomputing, 2016, 175: 310–323
Wu Y, PeiM, Yang M, Yuan J, Jia Y. Robust discriminative tracking via landmark-based label propagation. IEEE Transactions on Image Processing, 2015, 24(5): 1510–1523
Wang L, Liu T, Wang G, Chan K L, Yang Q. Video tracking using learned hierarchical features. IEEE Transactions on Image Processing, 2015, 24(4): 1424–1435
Zhang K, Liu Q, Wu Y, Yang M H. Robust visual tracking via convolutional networks without training. IEEE Transactions on Image Processing, 2016, 25(4): 1779–1792
Xu C, Tao W, Meng Z, Feng Z. Robust visual tracking via online multiple instance learning with fisher information. Pattern Recognition, 2015, 48(12): 3917–3926
Wang G, Qin X, Zhong F, Liu Y, Li H, Peng Q, Yang M. Visual tracking via sparse and local linear coding. IEEE Transactions on Image Processing, 2015, 24(11): 3796–3809
Sun X, Yao H, Zhang S, Li D. Non-rigid object contour tracking via a novel supervised level set model. IEEE Transactions on Image Processing, 2015, 24(11): 3386–3399
Sui Y, Zhang S, Zhang L. Robust visual tracking via sparsity-induced subspace learning. IEEE Transactions on Image Processing, 2015, 24(12): 4686–4700
Jang S I, Choi K, Toh K A, Teoh A B J, Kim J. Object tracking based on an online learning network with total error rate minimization. Pattern Recognition, 2015, 48(1): 126–139
Sun J, He F, Chen Y, Chen X. A multiple template approach for robust tracking of fast motion target. Applied Mathematics-A Journal of Chinese Universities, 2016, 31(2): 177–197
Hong-tu H, Du-yan B, Yu-fei Z, Shi-ping M, Shan G, Chang L. Robust visual tracking based on product sparse coding. Pattern Recognition Letters, 2015, 56: 52–59
Chen C, Li S, Qin H, Hao A. Real-time and robust object tracking in video via low-rank coherency analysis in feature space. Pattern Recognition, 2015, 48(9): 2885–2905
Zhang T, Liu S, Ahuja N, Yang M H, Ghanem B. Robust visual tracking via consistent low-rank sparse learning. International Journal of Computer Vision, 2015, 111(2): 171–190
Zhang X, Hu W, Xie N, Bao H, Maybank S. A robust tracking system for low frame rate video. International Journal of Computer Vision, 2015, 115(3): 279–304
Zhou Y, Bai X, Liu W, Latecki L J. Similarity fusion for visual tracking. International Journal of Computer Vision, 2016, 118(3): 337–363
Zhang D, He F, Han S, Zou L, WuY, Chen Y. An efficient approach to directly compute the exact Hausdorff distance for 3D point sets. Integrated Computer-Aided Engineering, 2017, 24(3), 261–277
Li X, Shen C, Dick A, Zhang Z M, Zhuang Y. Online metric-weighted linear representations for robust visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(5): 931–950
Zhang T, Liu S, Xu C, Yan S, Ghanem B, Ahuja N, Yang M H. Structural sparse tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015, 150–158
Li K, He F, Yu H P. Robust visual tracking based on convolutional features with illumination and occlusion handling. Journal of Computer Science and Technology, 2018, 33(1): 223–236
Liu T, Wang G, Yang Q. Real-time part-based visual tracking via adaptive correlation filters. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2015, 4902–4912
Chen Y L, He F Z, Wu Y Q, Hou N. A local start search algorithm to compute exact Hausdorff distance for arbitrary point sets. Pattern Recognition, 2017, 67: 139–148
Zhang Z, Hong Wong K. Pyramid-based visual tracking using sparsity represented mean transform. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014, 1226–1233
Zhang T, Jia K, Xu C, Ma Y, Ahuja N. Partial occlusion handling for visual tracking via robust part matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014, 1258–1265
Yu H P, He F, Pan Y. A novel region-based active contour model via local patch similarity measure for image segmentation. Multimedia Tools and Applications, 2018: 1–23
Danelljan M, Shahbaz Khan F, Felsberg M, Van de Weijer J. Adaptive color attributes for real-time visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014, 1090–1097
Yang M, Pei M T, Wu Y W, Jia Y. Learning online structural appearance model for robust object tracking. Science China Information Sciences, 2015, 58(3): 1–14
Smeulders A W, Chu D M, Cucchiara R, Calderara S, Dehghan A, Shah M. Visual tracking: an experimental survey. IEEE Transactions on Pattern Analysis andMachine Intelligence, 2014, 36(7): 1442–1468
Zhang H, Hu S, Yang G. Video object tracking based on appearance models learning. Journal of Computer Research and Development, 2015, 52(1): 177–190
Cehovin L, Leonardis A, Kristan M. Visual object tracking performance measures revisited. IEEE Transactions on Image Processing, 2016, 25(3): 1261–1274
Wu Y, Lim J, Yang M H. Online object tracking: a benchmark. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2013, 2411–2418
Ni B, He F, Pan Y, Yuan Z. Using shapes correlation for active contour segmentation of uterine fibroid ultrasound images in computeraided therapy. AppliedMathematics-A Journal of Chinese Universities, 2016, 31(1): 37–52
Yan X, He F, Hou N, Ai H. An efficient particle swarm optimization for large scale hardware/software co-design system. International Journal of Cooperative Information Systems, 2018, 27(1): 1741001
Yu Q, Dinh T B, Medioni G. Online tracking and reacquisition using co-trained generative and discriminative trackers. In: Proceedings of European Conference on Computer Vision. 2008, 678–691
Zhang D, He F, Han S, Li X. Quantitative optimization of interoperability during feature-based data exchange. Integrated Computer-Aided Engineering, 2016, 23(1): 31–51
Kwon J, Lee K M. Visual tracking decomposition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2010, 1269–1276
Ross D A, Lim J, Lin R S, Yang M H. Incremental learning for robust visual tracking. International Journal of Computer Vision, 2008, 77(1): 125–141
Belhumeur P N, Kriegman D J. What is the set of images of an object under all possible illumination conditions? International Journal of Computer Vision, 1998, 28(3): 245–260
Mei X, Ling H. Robust visual tracking using L1 minimization. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2009, 1436–1443
Bao C, Wu Y, Ling H, Ji H. Real time robust L1 tracker using accelerated proximal gradient approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2012, 1830–1837
Jia X, Lu H, Yang M H. Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2012, 1822–1829
Zhong W, Lu H, Yang M H. Robust object tracking via sparsity-based collaborative model. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2012, 1838–1845
Wang D, Lu H, Yang M H. Online object tracking with sparse prototypes. IEEE Transactions on Image Processing, 2013, 22(1): 314–325
Wang N, Wang J, Yeung D Y. Online robust non-negative dictionary learning for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision. 2013, 657–664
Zhang S, Yao H, Sun X, Lu X. Sparse coding based visual tracking: review and experimental comparison. Pattern Recognition, 2013, 46(7): 1772–1788
Babenko B, Yang M H, Belongie S. Visual tracking with online multiple instance learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2009, 983–990
Wang N, Shi J, Yeung D Y, Jia J. Understanding and diagnosing visual tracking systems. In: Proceedings of the IEEE International Conference on Computer Vision. 2015, 3101–3109
Hare S, Golodetz S, Saffari A, Vineet V, Cheng MM, Hicks S L, Torr P H. Struck: structured output tracking with kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(10): 2096–2109
Zhang K, Zhang L, Yang M. Real-time compressive tracking. In: Proceedings of European Conference on Computer Vision. 2012, 864–877
Kalal Z, Matas J, Mikolajczyk K. PN learning: bootstrapping binary classifiers by structural constraints. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2010, 49–56
Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1409–1422
Grabner H, Grabner M, Bischof H. Realtime tracking via on-line boosting. In: Proceedings of British Machine Vision Conference. 2006, 47–56
Arulampalam M S, Maskell S, Gordon N, Clapp T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 2002, 50(2): 174–188
Li K, He F, Ye H P, Chen X. A correlative classiffiers approach based on particle filter and sample set for tracking occluded target. Applied Mathematics-A Journal of Chinese Universities, 2017, 32(3): 294–312
Levey A, Lindenbaum M. Sequential Karhunen-Loeve basis extraction and its application to images. IEEE Transactions on Image Processing, 2000, 9(8): 1371–1374
Wang D, Lu H C, Yang M H. Least soft-thresold squares tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2013, 2371–2378
Wang D, Lu H. Visual tracking via probability continuous outlier model. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014, 3478–3485
Jia X, Lu H, Yang M H. Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2012, 1822–1829
Dinh T B, Vo N, Medioni G. Context tracker: exploring supporters and distracters in unconstrained environments. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2011, 1177–1184
Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2001, 511–518
Zhou Y, He F, Qiu Y. Optimization of parallel iterated local search algorithms on graphics processing unit. The Journal of Supercomputing, 2016, 72(6): 2394–2416
Zhou Y, He F, Qiu Y. Dynamic strategy based parallel ant colony optimization on GPUs for TSPs. Science China Information Sciences, 2017, 60(6): 068102
Wu Y, He F, Zhang D, Li X. Service-oriented feature-based data exchange for cloud-based design and manufacturing. IEEE Transactions on Services Computing, DOI 10.1109/TSC.2015.2501981
Zhou Y, He F, Hou N. Parallel ant colony optimization on multi-core simdcpus. Future Generation Computer Systems, 2018, 79(2): 473–487
Yan X, He F, Chen Y. A novel hardware/software partitioning method based on position disturbed particle swarm optimization with invasive weed optimization. Journal of Computer Science and Technology, 2017, 32(2): 340–355
Lv X, He F, Cai W. Supporting selective undo of string-wise operations for collaborative editing systems. Future Generation Computer Systems, 2018, 82: 41–62
Lv X, He F, Cai W, Cheng Y. A string-wise CRDT algorithm for smart and large-scale collaborative editing systems. Advanced Engineering Informatics, 2017, 33: 397–409
Zhu H, Nie Y, Yue T, Cao X. The role of prior in image based 3D modeling: a survey. Frontiers of Computer Science, 2017, 11(2): 175–191