Localizing object relatively with discrete wavelet transform feature through a similarity measure

Relativity improves localization of any object in visual aspects. Utilizing the same principle, this paper discusses about the relative localization of object of interest with DWT features and the classification of object with due reference of the background through a similarity measure. The proposed approach takes the advantage of DWT compressive features and effective distance measure making overall process very efficient and computationally inexpensive. Comparing proposed approach with four state-of-art algorithms on various video datasets, the experimental results show advancements in low center location error and visual object overlap rate.

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

[2]  David G. Stork,et al.  Pattern Classification , 1973 .

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

[4]  A RossDavid,et al.  Incremental Learning for Robust Visual Tracking , 2008 .

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

[6]  Horst Bischof,et al.  On-line Boosting and Vision , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

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

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

[9]  Jie Ma,et al.  Accurate Aerial Object Localization Using Gravity and Gravity Gradient Anomaly , 2015, IEEE Geoscience and Remote Sensing Letters.

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

[11]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[12]  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.

[13]  Jingdong Wang,et al.  Online Robust Non-negative Dictionary Learning for Visual Tracking , 2013, 2013 IEEE International Conference on Computer Vision.

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

[15]  Raymond T. Ng,et al.  Indexing spatio-temporal trajectories with Chebyshev polynomials , 2004, SIGMOD '04.

[16]  Horst Bischof,et al.  On-line Random Forests , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

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

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

[19]  Nan Jiang,et al.  Learning Adaptive Metric for Robust Visual Tracking , 2011, IEEE Transactions on Image Processing.

[20]  Fatih Murat Porikli,et al.  Covariance Tracking using Model Update Based on Lie Algebra , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

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

[22]  Narendra Ahuja,et al.  Robust Visual Tracking via Structured Multi-Task Sparse Learning , 2012, International Journal of Computer Vision.

[23]  Naitong Zhang,et al.  Human Localization Using Multi-Source Heterogeneous Data in Indoor Environments , 2017, IEEE Access.

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

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

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

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

[28]  Ming Tang,et al.  Robust tracking via weakly supervised ranking SVM , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[29]  Olivier Simonin,et al.  Localization of Humans, Objects, and Robots Interacting on Load-Sensing Floors , 2016, IEEE Sensors Journal.

[30]  Changsheng Xu,et al.  Deep Relative Tracking , 2017, IEEE Transactions on Image Processing.

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