Visual object tracking with online weighted chaotic multiple instance learning

The chaos theory has not been used for visual representation and tracking method before.Chaotic representation captures the complex dynamics of target.The chaotic dynamic can extract local and global information of object.The chaotic approximation method is suitable for online updating of model. In this paper, a chaotic multiple instance learning tracker based on chaos theory for a robust and efficient online tracking is introduced. In this method, chaotic characteristics can be utilized for representing the target as well as the updating appearance model, which has not been used for the tracking task. The computational architecture of the method is organized as follows. (1) Chaotic representation: a chaotic model can capture the complex dynamics of the target region to train the weak classifiers. Our representation can balance the global and local features to handle fast motion, partial occlusion, and illumination changes. (2) Importance of instance: fractal dimension of the dynamic model can be adjusted as instance weight for efficient online learning. (3) Chaotic approximation: A robust chaotic approximation to update the appearance model is introduced, which is crucial to select the discriminative and robust features. Chaotic online learning quickly explores the feature space to update the appearance model of the target by means of a chaotic map. The experimental results reveal that the proposed method is more effective and robust than the state-of-the-art trackers on various challenging sequences. Indeed, the efficiency of the proposed method is attributed to its strong online updating of chaotic policy as well as desirable target representation of chaotic model.

[1]  Ehud Rivlin,et al.  Mean Shift tracking with multiple reference color histograms , 2010, Comput. Vis. Image Underst..

[2]  Xiubao Sui,et al.  Adaptive pedestrian tracking via patch-based features and spatial-temporal similarity measurement , 2016, Pattern Recognit..

[3]  Yuan Xie,et al.  Online multiple instance gradient feature selection for robust visual tracking , 2012, Pattern Recognit. Lett..

[4]  Jun Li,et al.  Mutual information algorithms , 2010 .

[5]  Stan Z. Li,et al.  Online Spatio-temporal Structural Context Learning for Visual Tracking , 2012, ECCV.

[6]  Zdenek Kalal,et al.  Tracking-Learning-Detection , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Martin Casdagli,et al.  Nonlinear prediction of chaotic time series , 1989 .

[8]  Narendra Ahuja,et al.  Robust visual tracking via multi-task sparse learning , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Lei Zhang,et al.  Real-Time Compressive Tracking , 2012, ECCV.

[10]  Jiri Matas,et al.  P-N learning: Bootstrapping binary classifiers by structural constraints , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Xiangjian He,et al.  Robust visual tracking via efficient manifold ranking with low-dimensional compressive features , 2015, Pattern Recognit..

[12]  Ming-Hsuan Yang,et al.  Robust Object Tracking with Online Multiple Instance Learning , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Huchuan Lu,et al.  Sparse Hashing Tracking , 2016, IEEE Transactions on Image Processing.

[14]  Feiping Nie,et al.  Learning Instance Specific Distance for Multi-Instance Classification , 2011, AAAI.

[15]  M. Riley,et al.  IN FRACTAL PHYSIOLOGY , 2022 .

[16]  David Suter,et al.  Adaptive Object Tracking Based on an Effective Appearance Filter , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Gaoxiang Ouyang,et al.  Nonlinear similarity analysis for epileptic seizures prediction , 2006 .

[18]  Ming-Hsuan Yang,et al.  Visual tracking with online Multiple Instance Learning , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Stephen J. Merrill.  Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data , 2010 .

[20]  Wuzhen Shi,et al.  Improved appearance updating method in multiple instance learning tracking , 2014, IET Comput. Vis..

[21]  Yue Liu,et al.  Visual Tracking via Sparse and Local Linear Coding , 2015, IEEE Transactions on Image Processing.

[22]  Laura Sevilla-Lara,et al.  Distribution fields for tracking , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Huchuan Lu,et al.  Inverse Sparse Tracker With a Locally Weighted Distance Metric , 2015, IEEE Transactions on Image Processing.

[24]  Narendra Ahuja,et al.  Low-Rank Sparse Learning for Robust Visual Tracking , 2012, ECCV.

[25]  Ning Sun,et al.  Robust object tracking based on local region sparse appearance model , 2016, Neurocomputing.

[26]  Yuan Xie,et al.  Global Coupled Learning and Local Consistencies Ensuring for sparse-based tracking , 2015, Neurocomputing.

[27]  Zhang Hua,et al.  Visual Tracking Based on an Improved Online Multiple Instance Learning Algorithm , 2015, Comput. Intell. Neurosci..

[28]  Ehud Rivlin,et al.  Tracking by Affine Kernel Transformations Using Color and Boundary Cues , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Gang Wang,et al.  Video tracking using learned hierarchical features. , 2015, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society.

[30]  Peng Chen,et al.  Multiple instance learning tracking method with local sparse representation , 2013, IET Comput. Vis..

[31]  T. Moon.  The expectation-maximization algorithm , 1996, IEEE Signal Process. Mag..

[32]  Xiaoyu Zhang,et al.  Visual Tracking via Constrained Incremental Non-negative Matrix Factorization , 2015, IEEE Signal Processing Letters.

[33]  Qin Qin,et al.  Robust visual tracking based on incremental discriminative projective non-negative matrix factorization , 2015, Neurocomputing.

[34]  Xi Chen,et al.  Visual object tracking via online sparse instance learning , 2015, J. Vis. Commun. Image Represent..

[35]  Simone Calderara,et al.  Visual Tracking: An Experimental Survey , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[37]  Keiji Tatsumi,et al.  A sufficient condition for Chaos in the Gradient Model with perturbation Method for Global Optimization , 2013, Int. J. Bifurc. Chaos.

[38]  Kaihua Zhang,et al.  Real-time visual tracking via online weighted multiple instance learning , 2013, Pattern Recognit..

[39]  Fraser,et al.  Independent coordinates for strange attractors from mutual information. , 1986, Physical review. A, General physics.

[40]  Feiping Nie,et al.  Maximum Margin Multi-Instance Learning , 2011, NIPS.

[41]  Qi Tian,et al.  Online MIL tracking with instance-level semi-supervised learning , 2014, Neurocomputing.

[42]  Feiping Nie,et al.  Robust and discriminative distance for Multi-Instance Learning , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[43]  H. Abarbanel,et al.  Determining embedding dimension for phase-space reconstruction using a geometrical construction. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[44]  Haibin Ling,et al.  Robust Visual Tracking using 1 Minimization , 2009 .

[45]  Richard J. Povinelli,et al.  Time series classification using Gaussian mixture models of reconstructed phase spaces , 2004, IEEE Transactions on Knowledge and Data Engineering.

[46]  Ming-Hsuan Yang,et al.  Incremental Learning for Robust Visual Tracking , 2008, International Journal of Computer Vision.

[47]  Feng Liu,et al.  From sample selection to model update: A robust online visual tracking algorithm against drifting , 2016, Neurocomputing.

[48]  Wuzhen Shi,et al.  Visual tracking based on Distribution Fields and online weighted multiple instance learning , 2013, Image Vis. Comput..

[49]  Dapeng Tao,et al.  Online tracking based on efficient transductive learning with sample matching costs , 2016, Neurocomputing.

[50]  F. Takens.  Detecting strange attractors in turbulence , 1981 .