Robust visual tracking via bag of superpixels

The Bag of Words (BoW) model is one of the most popular and effective image representation methods and has been drawn increasing interest in computer vision filed. However, little attention is paid on it in visual tracking. In this paper, a visual tracking method based on Bag of Superpixels (BoS) is proposed. In BoS, the training samples are oversegmented to generate enough superpixel patches. Then K-means algorithm is performed on the collected patches to form visual words of the target and a superpixel codebook is constructed. Finally the tracking is accomplished via searching for the highest likelihood between candidates and codebooks within Bayesian inference framework. In this process, an effective updating scheme is adopted to help our tracker resist occlusions and deformations. Experimental results demonstrate that the proposed method outperforms several state-of-the-art trackers.

[1]  Andreas E. Savakis,et al.  Online Distance Metric Learning for Object Tracking , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[2]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[3]  Huchuan Lu,et al.  Robust Superpixel Tracking , 2014, IEEE Transactions on Image Processing.

[4]  Jie Yang,et al.  A novel fragments-based tracking algorithm using mean shift , 2008, 2008 10th International Conference on Control, Automation, Robotics and Vision.

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

[6]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Filiberto Pla,et al.  Bag-of-words with aggregated temporal pair-wise word co-occurrence for human action recognition , 2014, Pattern Recognit. Lett..

[8]  Jianguo Zhang,et al.  The PASCAL Visual Object Classes Challenge , 2006 .

[9]  Ehud Rivlin,et al.  Robust Fragments-based Tracking using the Integral Histogram , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[10]  Patricio A. Vela,et al.  A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm , 2012, Expert Syst. Appl..

[11]  Wenyu Liu,et al.  Bag of contour fragments for robust shape classification , 2014, Pattern Recognit..

[12]  Fei Yang,et al.  Visual tracking via bag of features , 2012 .

[13]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[14]  Ioannis Pratikakis,et al.  Bag of spatio-visual words for context inference in scene classification , 2013, Pattern Recognit..

[15]  Jun Xu,et al.  Part-Based Visual Tracking via Online Weighted P-N Learning , 2014, TheScientificWorldJournal.

[16]  Lei Zhang,et al.  Fast Compressive Tracking , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Alexei A. Efros,et al.  Discovering objects and their location in images , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[18]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[19]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[20]  Zhongfei Zhang,et al.  A survey of appearance models in visual object tracking , 2013, ACM Trans. Intell. Syst. Technol..

[21]  ZissermanAndrew,et al.  The Pascal Visual Object Classes Challenge , 2015 .

[22]  Xiaoping Du,et al.  Robust tracking based on local structural cell graph , 2015, J. Vis. Commun. Image Represent..

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

[24]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[25]  Alexandros Iosifidis,et al.  Discriminant Bag of Words based representation for human action recognition , 2014, Pattern Recognit. Lett..

[26]  Gao Wen,et al.  Real-time object tracking via online weighted multiple instance learning , 2014 .

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

[28]  Pietro Perona,et al.  Learning object categories from Google's image search , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[29]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[30]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

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

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

[33]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.