Visual object tracking based on residual network and cascaded correlation filters

Bertinetto L, Valmadre J, Golodetz S, Miksik O, Torr PHS (2016a) Staple: complementary learners for real-time tracking. In: Proceedings of the 2016 IEEE conference on computer vision and pattern recognition, pp 1401–1409

Bertinetto L, Valmadre J, Henriques JF, Vedaldi A, Torr PHS (2016b) Fully-convolutional siamese networks for object tracking. In: Proceedings of the 2016 European conference on computer vision, vol 9914, pp 850–865

Bolme DS, Beveridge JR, Draper BA, Lui YM (2010) Visual object tracking using adaptive correlation filters. In: Proceedings of the 2010 IEEE conference on computer vision and pattern recognition. IEEE Computer Society, pp 2544–2550

Chen Y, Wang J, Xia R, Zhang Q, Cao Z, Yang K (2019) The visual object tracking algorithm research based on adaptive combination kernel. J Ambient Intell Hum Comput 10(12):4855–4867

Chen Y, Xiong J, Xu W, Zuo J (2019) A novel online incremental and decremental learning algorithm based on variable support vector machine. Cluster Comput 22(S3):7435–7445

Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceedings of the 2005 IEEE conference on computer vision and pattern recognition, pp 886–893

Danelljan M, H¨ager G, Khan FS, Felsberg M (2014a) Accurate scale estimation for robust visual tracking. In: Proceedings of the 2014 British machine vision conference. BMVA Press

Danelljan M, H¨ager G, Khan FS, Felsberg M (2015) Learning spatially regularized correlation filters for visual tracking. In: Proceedings of the 2015 IEEE international conference on computer vision. IEEE Computer Society, pp 4310–4318

Danelljan M, Khan FS, Felsberg M, van deWeijer J (2014b) Adaptive color attributes for real-time visual tracking. In: Proceedings of the 2014 IEEE conference on computer vision and pattern recognition, pp 1090–1097

Danelljan M, Robinson A, Khan FS, Felsberg M (2016) Beyond correlation filters: learning continuous convolution operators for visual tracking. In: Proceedings of the 2016 European conference on computer vision, Springer, vol 9909, pp 472–488

Danelljan M, Bhat G, Khan FS, Felsberg M (2017) ECO: efficient convolution operators for tracking. In: Proceedings of the 2017 IEEE conference on computer vision and pattern recognition. IEEE Computer Society, pp 6931–6939

Fan H, Ling H (2017a) Parallel tracking and verifying: a framework for real-time and high accuracy visual tracking. In: Proceedings of the 2017 IEEE international conference on computer vision, IEEE Computer Society, pp 5487–5495

Fan H, Ling H (2017b) SANet: structure-aware network for visual tracking. In: Proceedings of the 2017 IEEE conference on computer vision and pattern recognition, IEEE Computer Society, pp 2217–2224

Fan H, Ling H (2019) Siamese cascaded region proposal networks for real-time visual tracking. In: Proceedings of the 2019 IEEE conference on computer vision and pattern recognition, IEEE Computer Vision Foundation, pp 7952–7961

Fan H, Lin L, Yang F, Chu P, Deng G, Yu S, Bai H, Xu Y, Liao C, Ling H (2019) LaSOT: a high-quality benchmark for large-scale single object tracking. In: Proceedings of the 2019 IEEE conference on computer vision and pattern recognition, pp 5374–5383

Gao Z, Xia S, Zhang Y, Yao R, Zhao J, Niu Q, Jiang H (2018) Real-time visual tracking with compact shape and color feature. Comput Mater Contin 55(3):509–521

He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE conference on computer vision and pattern recognition, IEEE Computer Society, pp 770–778

Henriques JF, Caseiro R, Martins P, Batista JP (2012) Exploiting the circulant structure of tracking-by-detection with kernels. In: Proceedings of the 2012 European conference on computer vision, Springer, vol 7575, pp 702–715

Henriques JF, Caseiro R, Martins P, Batista J (2015) Highspeed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell 37(3):583–596

Li Y, Zhu J (2014) A scale adaptive kernel correlation filter tracker with feature integration. In: Proceedings of the 2014 European conference on computer vision, Springer, vol 8926, pp 254–265

Li F, Tian C, Zuo W, Zhang L, Yang M (2018) Learning spatial-temporal regularized correlation filters for visual tracking. In: Proceedings of the 2018 IEEE conference on computer vision and pattern recognition, IEEE Computer Society, pp 4904–4913

Liang P, Blasch E, Ling H (2015) Encoding color information for visual tracking: algorithms and benchmark. IEEE Trans Image Process 24(12):5630–5644

Liu W, Liu Z, Wang L, Li B, Jing N (2018) Human movement detection and gait periodicity analysis via channel state information. Comput Syst Sci Eng 33(2)

Liu F, Guo Y, Cai Z, Xiao N, Zhao Z (2019) Edge-enabled disaster rescue: a case study of searching for missing people. ACM Trans Intell Syst Technol 10(6): 63:1–63:21

Ma C, Huang J, Yang X, Yang M (2015) Hierarchical convolutional features for visual tracking. In: Proceedings of the 2015 IEEE international conference on computer vision, IEEE Computer Society, pp 3074–3082

Mueller M, Smith N, Ghanem B (2016) A benchmark and simulator for UAV tracking. In: Proceedings of the 2016 European conference on computer vision, Springer, pp 445–461

Possegger H, Mauthner T, Bischof H (2015) In defense of color-based model-free tracking. In: Proceedings of the 2015 IEEE conference on computer vision and pattern recognition, IEEE Computer Society, pp 2113–2120

Viola PA, Jones MJ (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE conference on computer vision and pattern recognition, pp 511–518

Wang N, Yeung D (2013) Learning a deep compact image representation for visual tracking. In: 27th annual conference on neural information processing systems, pp 809–817

Wang J, Ju C, Gao Y, Sangaiah AK, Kim G (2018) A PSO based energy efficient coverage control algorithm for wireless sensor networks. Comput Mater Contin 56(3):433–446

Wang J, Gao Y, Liu W, Wu W, Lim SJ (2019) An asynchronous clustering and mobile data gathering schema based on timer mechanism in wireless sensor networks. Comput Mater Contin 58:711–725

Wang Q, Zhang L, Bertinetto L, Hu W, Torr PHS (2019b) Fast online object tracking and segmentation: a unifying approach. In: Proceedings of the 2019 IEEE conference on computer vision and pattern recognition, IEEE Computer Vision Foundation, pp 1328–1338

Wang J, Gao Y, Zhou C, Sherratt S, Wang L (2020) Optimal coverage multi-path scheduling scheme with multiple mobile sinks for wsns. Comput Mater Contin 62(2):695–711

Wu Y, Lim J, Yang M (2013) Online object tracking: a benchmark. In: Proceedings of the 2013 IEEE conference on computer vision and pattern recognition, pp 2411–2418

Wu Y, Lim J, Yang M (2015) Object tracking benchmark. IEEE Trans Pattern Anal Mach Intell 37(9):1834–1848

Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4):13

Zhang J, Ma S, Sclaroff S (2014) MEEM: robust tracking via multiple experts using entropy minimization. In: Proceedings of the 2014 European conference on computer vision, Springer, vol 8694, pp 188–203

Zhang J, Jin X, Sun J, Wang J, Li K (2019) Dual model learning combined with multiple feature selection for accurate visual tracking. IEEE Access 7:43956–43969

Zhang J, Wu Y, Feng W, Wang J (2019) Spatially attentive visual tracking using multi-model adaptive response fusion. IEEE Access 7:83873–83887

Zhang D, Yang G, Li F, Wang J, Sangaiah AK (2020) Detecting seam carved images using uniform local binary patterns. Multimed Tools Appl 79(13–14):8415–8430

Zhang J, Jin X, Sun J, Wang J, Sangaiah AK (2020) Spatial and semantic convolutional features for robust visual object tracking. Multimed Tools Appl 79(21–22):15095–15115

Zhang J, Lu C, Wang J, Yue X, Lim S, Al-Makhadmeh Z, Tolba A (2020) Training convolutional neural networks with multi-size images and triplet loss for remote sensing scene classification. Sensors 20(4):1188

Zhang J, Wang W, Lu C, Wang J, Sangaiah AK (2020) Lightweight deep network for traffic sign classification. Ann Telecommun 75(7–8):369–379

Zhang J, Xie Z, Sun J, Zou X, Wang J (2020) A cascaded R-CNN with multiscale attention and imbalanced samples for traffic sign detection. IEEE Access 8:29742–29754

Zhou Z, Qin J, Xiang X, Tan Y, Liu Q, Xiong NN (2020) News text topic clustering optimized method based on TF-IDF algorithm on spark. Comput Mater Contin 62(1):217–231