An Implementation of a Robust Visual Object Tracking System

Robust visual tracking system with high level of accuracy against complex tracking scenarios containing many visual attributes has been a challenging research topic in computer vision field. Among different types of visual attributes, scale variation is considered as one of the most difficult problems. Existing tracking methods either fail to handle great change of target size, or employ exhaustive scale estimation with the cost of high computational load. This paper presents a method of accurately adapting the change in target scale which employs dense spatio-temporal context for localizing target position, with the intention to increase tracking performance while maintaining low computational cost. In particular, the tracking task for determining target position is computed by utilizing the spatio-temporal context relationship between the target and its surrounding regions. Then the spatial correlation is analyzed to update target position in subsequent frames. In the meantime, the model also estimates the change of target by applying a scale filter, which learns the change in appearance of a set of various value of scales. Finally, the proposed method is evaluated by using the image sequences in TB100 dataset with different performance evaluation tests.

[1]  Dit-Yan Yeung,et al.  Learning a Deep Compact Image Representation for Visual Tracking , 2013, NIPS.

[2]  Michael Felsberg,et al.  Discriminative Scale Space Tracking , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  David Zhang,et al.  Fast Visual Tracking via Dense Spatio-temporal Context Learning , 2014, ECCV.

[4]  Philip H. S. Torr,et al.  Struck: Structured output tracking with kernels , 2011, ICCV.

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

[6]  David J. Fleet,et al.  Robust Online Appearance Models for Visual Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Takahiro Ishikawa,et al.  The template update problem , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  John K. Tsotsos,et al.  Fast Visual Object Tracking with Rotated Bounding Boxes , 2019, ArXiv.

[10]  Gang Hua,et al.  Context-Aware Visual Tracking , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[12]  Haibin Ling,et al.  Robust visual tracking using ℓ1 minimization , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[13]  Haibin Ling,et al.  Robust Visual Tracking and Vehicle Classification via Sparse Representation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[15]  Rynson W. H. Lau,et al.  Visual Tracking via Locality Sensitive Histograms , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Huchuan Lu,et al.  Visual tracking via adaptive structural local sparse appearance model , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Ken Chen,et al.  A Meanshift-based imbedded computer vision system design for real-time target tracking , 2012, 2012 7th International Conference on Computer Science & Education (ICCSE).

[18]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

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

[20]  Jin Gao,et al.  Transfer Learning Based Visual Tracking with Gaussian Processes Regression , 2014, ECCV.

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

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

[23]  Luc Van Gool,et al.  Learning Discriminative Model Prediction for Tracking , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

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

[25]  Philippe C. Cattin,et al.  Tracking the invisible: Learning where the object might be , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[27]  Ying Wu,et al.  Discriminative Spatial Attention for Robust Tracking , 2010, ECCV.

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

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

[30]  Vibhav Vineet,et al.  Struck: Structured Output Tracking with Kernels , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.