Uncertain Motion Tracking Combined Markov Chain Monte Carlo and Correlation Filters

To address the uncertain motion tracking problem, a tracking method based on the Markov Chain Monte Carlo and correlation filters is proposed. Firstly, multi-scope marginal likelihood (MSML) strategy is introduced to Wang-Landau Monte Carlo (WLMC) tracking method for increasing the acceptance ratio of samples in the promising regions and obtaining a more reliable distribution of density-of-states (DOS). Secondly, in order to raise the efficiency of the tracker, DOS is used to mark the region of interest. Then correlation filters are used to simplify the iterative optimizing operation of the subregions, and eventually target positioning is achieved by maximum response in the promising regions. Finally, a unified tracking framework is designed to enable correlation filters and WLMC with MSML strategy to exploit and complement each other to cope with uncertain motion tracking. Extensive experimental results on uncertain Motion sequences and benchmark datasets demonstrate that the proposed method performs favorably against the state-of-the-art methods.

[1]  Luca Bertinetto,et al.  Staple: Complementary Learners for Real-Time Tracking , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Zhigang Zeng,et al.  Memristor-based circuit implementation of pulse-coupled neural network with dynamical threshold generators , 2018, Neurocomputing.

[3]  Zhigang Zeng,et al.  A modified Elman neural network with a new learning rate scheme , 2018, Neurocomputing.

[4]  T. Poggio,et al.  Regularized Least-Squares Classification 133 In practice , although , 2007 .

[5]  D. Landau,et al.  Efficient, multiple-range random walk algorithm to calculate the density of states. , 2000, Physical review letters.

[6]  Rynson W. H. Lau,et al.  VITAL: VIsual Tracking via Adversarial Learning , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[7]  Ming-Hsuan Yang,et al.  Hierarchical Convolutional Features for Visual Tracking , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[8]  Michael Felsberg,et al.  Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking , 2016, ECCV.

[9]  Yao Lu,et al.  Abrupt motion tracking via adaptive stochastic approximation Monte Carlo sampling , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Tianzhu Zhang,et al.  In Defense of Sparse Tracking: Circulant Sparse Tracker , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[12]  Xiaoqin Zhang,et al.  Contour tracking with abrupt motion , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[13]  Junxing Zhang,et al.  Object tracking using Langevin Monte Carlo particle filter and locality sensitive histogram based likelihood model , 2018, Comput. Graph..

[14]  Andrew Blake,et al.  Articulated body motion capture by annealed particle filtering , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[15]  Junseok Kwon,et al.  Visual tracking decomposition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Harish Bhaskar,et al.  Online discriminative dictionary learning for robust object tracking , 2018, Neurocomputing.

[17]  Jian Zhang,et al.  Abrupt Motion Tracking via Nearest Neighbor Field Driven Stochastic Sampling , 2015, Neurocomputing.

[18]  Yuan Li,et al.  Tracking in Low Frame Rate Video: A Cascade Particle Filter with Discriminative Observers of Different Lifespans , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Qi Tian,et al.  Multi-cue Correlation Filters for Robust Visual Tracking , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[20]  Zhigang Zeng,et al.  Fuzzy Control for Uncertain Vehicle Active Suspension Systems via Dynamic Sliding-Mode Approach , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[21]  Rui Caseiro,et al.  High-Speed Tracking with Kernelized Correlation Filters , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Qingjie Zhao,et al.  Objectness-based smoothing stochastic sampling and coherence approximate nearest neighbor for visual tracking , 2018, The Visual Computer.

[23]  Luca Bertinetto,et al.  Fully-Convolutional Siamese Networks for Object Tracking , 2016, ECCV Workshops.

[24]  Huanlong Zhang,et al.  SIFT flow for abrupt motion tracking via adaptive samples selection with sparse representation , 2017, Neurocomputing.

[25]  Alberto Del Bimbo,et al.  Particle filter-based visual tracking with a first order dynamic model and uncertainty adaptation , 2011, Comput. Vis. Image Underst..

[26]  Cordelia Schmid,et al.  Online Object Tracking with Proposal Selection , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[27]  Harish Bhaskar,et al.  Graph Regularized and Locality-Constrained Coding for Robust Visual Tracking , 2017, IEEE Transactions on Circuits and Systems for Video Technology.

[28]  Zhigang Zeng,et al.  General memristor with applications in multilayer neural networks , 2018, Neural Networks.

[29]  Zhenyu He,et al.  Target-Aware Deep Tracking , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[30]  Lei Luo,et al.  Enable Scale and Aspect Ratio Adaptability in Visual Tracking with Detection Proposals , 2015, BMVC.

[31]  Michael Felsberg,et al.  Accurate Scale Estimation for Robust Visual Tracking , 2014, BMVC.

[32]  Frank Dellaert,et al.  MCMC-based particle filtering for tracking a variable number of interacting targets , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Tao Zhou,et al.  Online learning and joint optimization of combined spatial-temporal models for robust visual tracking , 2017, Neurocomputing.

[34]  Bingbing Ni,et al.  Deep Regression Tracking with Shrinkage Loss , 2018, ECCV.

[35]  Jianke Zhu,et al.  A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration , 2014, ECCV Workshops.

[36]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[37]  Louahdi Khoudour,et al.  Robust visual tracking via MCMC-based particle filtering , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[38]  Michael Felsberg,et al.  Convolutional Features for Correlation Filter Based Visual Tracking , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[39]  Changsheng Xu,et al.  Multi-task Correlation Particle Filter for Robust Object Tracking , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[40]  Jiwen Lu,et al.  Abrupt Motion Tracking Via Intensively Adaptive Markov-Chain Monte Carlo Sampling , 2012, IEEE Transactions on Image Processing.

[41]  Junseok Kwon,et al.  Tracking of Abrupt Motion Using Wang-Landau Monte Carlo Estimation , 2008, ECCV.

[42]  Ming-Hsuan Yang,et al.  Object Tracking Benchmark , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[43]  Qingming Huang,et al.  Hedged Deep Tracking , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[44]  Junseok Kwon,et al.  Wang-Landau Monte Carlo-Based Tracking Methods for Abrupt Motions , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[45]  Qiang Wang,et al.  DCFNet: Discriminant Correlation Filters Network for Visual Tracking , 2017, ArXiv.

[46]  Ming-Hsuan Yang,et al.  Robust Visual Tracking via Hierarchical Convolutional Features , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[47]  Ming-Hsuan Yang,et al.  Long-term correlation tracking , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[48]  Yi Wu,et al.  Online Object Tracking: A Benchmark , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[49]  Rui Caseiro,et al.  Exploiting the Circulant Structure of Tracking-by-Detection with Kernels , 2012, ECCV.

[50]  A. Gelman,et al.  Weak convergence and optimal scaling of random walk Metropolis algorithms , 1997 .

[51]  Zhigang Zeng,et al.  Aperiodic Sampled-Data Sliding-Mode Control of Fuzzy Systems With Communication Delays Via the Event-Triggered Method , 2016, IEEE Transactions on Fuzzy Systems.

[52]  Stan Sclaroff,et al.  MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization , 2014, ECCV.

[53]  Gareth O. Roberts,et al.  Examples of Adaptive MCMC , 2009 .

[54]  Bernard Ghanem,et al.  Context-Aware Correlation Filter Tracking , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[55]  Mingyu Lu,et al.  Ordered over-relaxation based Langevin Monte Carlo sampling for visual tracking , 2017, Neurocomputing.

[56]  Dongbing Gu,et al.  Abrupt motion tracking using a visual saliency embedded particle filter , 2014, Pattern Recognit..

[57]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[58]  Bruce A. Draper,et al.  Visual object tracking using adaptive correlation filters , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[59]  Shai Avidan,et al.  Locally Orderless Tracking , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.