Fully-Convolutional Siamese Networks for Object Tracking

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741

FaceNet: A unified embedding for face recognition and clustering

Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267

DeepFace: Closing the Gap to Human-Level Performance in Face Verification

Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542

High-Speed Tracking with Kernelized Correlation Filters

Henriques, J. F., Caseiro, R., Martins, P., & Batista, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2345390 · Cited by 5,753

Deep Face Recognition

Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015) · Procedings of the British Machine Vision Conference 2015 · doi:10.5244/c.29.41 · Cited by 5,479

CNN Features Off-the-Shelf: An Astounding Baseline for Recognition

Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2014.131 · Cited by 4,451

Flownet: Learning optical flow with convolutional networks

Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Smagt, P. V. D., Cremers, D., & Brox, T. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.316 · Cited by 4,313

Online Object Tracking: A Benchmark

Wu, Y., Lim, J., & Yang, M.-H. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.312 · Cited by 4,138

Siamese Neural Networks for One-shot Image Recognition

Koch, G. E., Zemel, R. S., & Salakhutdinov, R. (2015) · Cited by 3,712

Tracking-Learning-Detection

Kalal, Z., Mikolajczyk, K., & Matas, J. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.239 · Cited by 3,332

Learning Multi-domain Convolutional Neural Networks for Visual Tracking

Nam, H., & Han, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.465 · Cited by 2,806

MatConvNet: Convolutional Neural Networks for MATLAB

Vedaldi, A., & Lenc, K. (2015) · Proceedings of the 23rd ACM international conference on Multimedia · doi:10.1145/2733373.2807412 · Cited by 2,444

SIGNATURE VERIFICATION USING A “SIAMESE” TIME DELAY NEURAL NETWORK

BROMLEY, J., BENTZ, J. W., BOTTOU, L., GUYON, I., LECUN, Y., MOORE, C., SÄCKINGER, E., & SHAH, R. (1994) · Series in Machine Perception and Artificial Intelligence · doi:10.1142/9789812797926_0003 · Cited by 2,350

Learning Spatially Regularized Correlation Filters for Visual Tracking

Danelljan, M., Hager, G., Khan, F. S., & Felsberg, M. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.490 · Cited by 2,180

Accurate Scale Estimation for Robust Visual Tracking

Danelljan, M., Hager, G., Khan, F. S., & Felsberg, M. (2014) · British Machine Vision Conference (BMVC) · doi:10.5244/c.28.65 · Cited by 2,145

Signature Verification Using A "Siamese" Time Delay Neural Network

BROMLEY, J., BENTZ, J. W., BOTTOU, L., GUYON, I., LECUN, Y., MOORE, C., SÄCKINGER, E., & SHAH, R. (1993) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001493000339 · Cited by 2,077

Struck: Structured output tracking with kernels

Hare, S., Saffari, A., & Torr, P. H. S. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126251 · Cited by 1,831

Staple: Complementary Learners for Real-Time Tracking

Bertinetto, L., Valmadre, J., Golodetz, S., Mikšík, O., & Torr, P. H. S. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.156 · Cited by 1,825

Hierarchical Convolutional Features for Visual Tracking

Ma, C., Huang, J., Yang, X., & Yang, M. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.352 · Cited by 1,819

Visual Tracking: An Experimental Survey

Smeulders, A. W. M., Chu, D. M., Cucchiara, R., Calderara, S., Dehghan, A., & Shah, M. A. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.230 · Cited by 1,527

A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration

Li, Y., & Zhu, J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16181-5_18 · Cited by 1,461

Siamese Instance Search for Tracking

Tao, R., Gavves, E., & Smeulders, A. W. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.158 · Cited by 1,208

Learning to Track at 100 FPS with Deep Regression Networks

Held, D., Thrun, S., & Savarese, S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_45 · Cited by 1,199

Learning to compare image patches via convolutional neural networks

Zagoruyko, S., & Komodakis, N. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299064 · Cited by 1,140

Visual Tracking with Fully Convolutional Networks

Wang, L., Ouyang, W., Wang, X., & Lu, H. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.357 · Cited by 1,012

Convolutional Features for Correlation Filter Based Visual Tracking

Danelljan, M., Hager, G., Khan, F. S., & Felsberg, M. (2015) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2015.84 · Cited by 1,008

Long-term correlation tracking

Ma, C., Yang, X., Zhang, C., & Yang, M. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299177 · Cited by 980

The Visual Object Tracking VOT2015 Challenge Results

Kristan, M., Leonardis, A., Matas, J., Felsberg, M., Pflugfelder, R., Zajc, L. Č., Vojír, T., Häger, G., Lukežič, A., Eldesokey, A., et al. (2015) · 2015 IEEE International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2015.79 · Cited by 895

Discriminative Learning of Deep Convolutional Feature Point Descriptors

Simo‐Serra, E., Trulls, E., Ferraz, L., Kokkinos, I., Fua, P., & Moreno-Noguer, F. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.22 · Cited by 818

FlowNet: Learning Optical Flow with Convolutional NetworksPreprint

Fischer, P., Dosovitskiy, A., Ilg, E., Haeusser, P., Hazırbaş, C., Golkov, V., van der Smagt, P., Cremers, D., & Brox, T. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.06852 · Cited by 814

Efficient Deep Learning for Stereo Matching

Luo, W., Schwing, A. G., & Urtasun, R. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.614 · Cited by 764

The Visual Object Tracking VOT2014 Challenge Results

Kristan, M., Pflugfelder, R. P., Leonardis, A., Matas, J., Zajc, L. Č., Nebehay, G., Vojíř, T., Fernández, G., Lukežič, A., Dimitriev, A., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16181-5_14 · Cited by 606

In defense of color-based model-free tracking

Possegger, H., Mauthner, T., & Bischof, H. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298823 · Cited by 370

Transferring Rich Feature Hierarchies for Robust Visual TrackingPreprint

Wang, N., Li, S., Gupta, A. K., & Yeung, D. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1501.04587 · Cited by 307

Object Tracking via Dual Linear Structured SVM and Explicit Feature Map

Ning, J., Yang, J., Jiang, S., Lei Zhang, A., & Yang, M. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.462 · Cited by 250

Learning Where to Attend with Deep Architectures for Image Tracking

Denil, M., Bazzani, L., Larochelle, H., & de Freitas, N. (2012) · Neural Computation · doi:10.1162/neco_a_00312 · Cited by 199

Visual Tracking Using Attention-Modulated Disintegration and Integration

Choi, J., Chang, H. J., Jeong, J., Demiris, Y., & Choi, J. Y. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.468 · Cited by 191

NUS-PRO: A New Visual Tracking Challenge

Li, A., Lin, M., Wu, Y., Yang, M., & Yan, S. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2417577 · Cited by 174

Online Object Tracking with Proposal Selection

Yang, H., Alahari, K., & Schmid, C. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.354 · Cited by 106

Once for All: A Two-Flow Convolutional Neural Network for Visual Tracking

Chen, K., & Tao, W. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2757061 · Cited by 94

Ensemble-Based Tracking: Aggregating Crowdsourced Structured Time Series Data

Wang, N., & Yeung, D. (2014) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · Cited by 91

RATM: Recurrent Attentive Tracking Model

Kahou, S. E., Michalski, V., Memisevic, R., Pal, C., & Vincent, P. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.206 · Cited by 85

Enable Scale and Aspect Ratio Adaptability in Visual Tracking with Detection Proposals

Huang, D., Luo, L., Wen, M., Chen, Z., & Zhang, C. (2015) · British Machine Vision Conference (BMVC) · doi:10.5244/c.29.185 · Cited by 73

Collaborative Correlation Tracking

Zhu, G., Wang, J., Wu, Y., & Lu, H. (2015) · British Machine Vision Conference (BMVC) · doi:10.5244/c.29.184 · Cited by 59

First Step toward Model-Free, Anonymous Object Tracking with Recurrent Neural NetworksPreprint

Gan, Q., Guo, Q., Zhang, Z., & Cho, K. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.06425 · Cited by 53

Tracking Randomly Moving Objects on Edge Box ProposalsPreprint

Zhu, G., Porikli, F., & Li, H. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.08085 · Cited by 43

Exploring Simple Siamese Representation Learning

Chen, X., & He, K. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01549 · Cited by 3,725

High Performance Visual Tracking with Siamese Region Proposal Network

Li, B., Yan, J., Wu, W., Zhu, Z., & Hu, X. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00935 · Cited by 3,023

ECO: Efficient Convolution Operators for Tracking

Danelljan, M., Bhat, G., Khan, F. S., & Felsberg, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.733 · Cited by 2,805

ByteTrack: Multi-object Tracking by Associating Every Detection Box

Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., Liu, W., & Wang, X. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20047-2_1 · Cited by 3,040

SiamRPN++: Evolution of Siamese Visual Tracking With Very Deep Networks

Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., & Yan, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00441 · Cited by 2,607

Deep Feature Based Siamese Network for Visual Object Tracking

Lim, S.-C., Huh, J., & Kim, J.-C. (2022) · Energies · doi:10.3390/en15176388 · Cited by 3

Real-time object tracking based on improved fully-convolutional siamese network

Xu, H., & Zhu, Y. (2020) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2020.106755 · Cited by 12

Template Update Based on 3D-Convolutional Siamese Network for Object Tracking

Lu, X., Wang, X., Wang, Z., & Hei, X. (2021) · Asia Conference on Computers and Communications (ACCC) · doi:10.1109/accc54619.2021.00016

Visual tracking using Siamese convolutional neural network with region proposal and domain specific updating

Zhang, H., Ni, W., Yan, W., Wu, J., Hui, B., & Xiang, D. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.11.050 · Cited by 28

Siamese Trackers Based on Deep Features for Visual Tracking

Lim, S.-C., Huh, J., & Kim, J.-C. (2023) · Electronics · doi:10.3390/electronics12194140 · Cited by 1