Visual Object Tracking by Structure

Appearance change of moving targets is a challenging problem in visual tracking. In this paper, we present a novel visual object tracking algorithm based on the observation dependent hidden Markov model (OD-HMM) framework. The observation dependency is computed by structure complexity coefficients (SCC) which is defined to predict the target appearance change. Unlike conventional methods addressing the appearance change problem by investigating different online appearance models, we handle this problem by addressing the fundamental reason of motion-related appearance change during visual tracking. Based on the analysis of motion-related appearance change, we investigate the relationship between the structure of the object surface and the appearance stability. The appearance of complex structural regions is easier to change compared with that of smooth structural regions with object moving. Based on this, we define SCC to predict the appearance stability of moving objects. Different from the standard HMM-based tracking algorithms where observations between different frames are assumed to be independent, we consider the observation dependency between consecutive frames with the information provided by SCC. Moreover, we present a novel outlier removing method in appearance model updating which helps to avoid error accumulation. Experimental results on challenging video sequences demonstrate that the proposed visual tracking algorithm with OD-HMM and SCC achieves better performance than existing related tracking algorithms.

[1]  Weisi Lin,et al.  Visual Object Tracking Based on Backward Model Validation , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

[2]  Jenq-Neng Hwang,et al.  Tracking Human Under Occlusion Based on Adaptive Multiple Kernels With Projected Gradients , 2013, IEEE Transactions on Multimedia.

[3]  Junseok Kwon,et al.  Highly Nonrigid Object Tracking via Patch-Based Dynamic Appearance Modeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[5]  Huchuan Lu,et al.  Least Soft-Threshold Squares Tracking , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

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

[7]  Huchuan Lu,et al.  On-line learning parts-based representation via incremental orthogonal projective non-negative matrix factorization , 2013, Signal Process..

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

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

[10]  Junseok Kwon,et al.  Robust visual tracking using autoregressive hidden Markov Model , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Feng Li,et al.  Blurred target tracking by Blur-driven Tracker , 2011, 2011 International Conference on Computer Vision.

[12]  Philip H. S. Torr,et al.  Struck: Structured output tracking with kernels , 2011, 2011 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]  Lina J. Karam,et al.  A No-Reference Image Blur Metric Based on the Cumulative Probability of Blur Detection (CPBD) , 2011, IEEE Transactions on Image Processing.

[15]  Luc Van Gool,et al.  Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[17]  Horst Bischof,et al.  PROST: Parallel robust online simple tracking , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  Lifeng Sun,et al.  Contextual Mixture Tracking , 2009, IEEE Transactions on Multimedia.

[19]  Horst Bischof,et al.  Semi-supervised On-Line Boosting for Robust Tracking , 2008, ECCV.

[20]  Jiaya Jia,et al.  Image partial blur detection and classification , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

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

[22]  M. Shah,et al.  Object tracking: A survey , 2006, CSUR.

[23]  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).

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

[25]  P. Bex,et al.  Spatial frequency, phase, and the contrast of natural images. , 2002, Journal of the Optical Society of America. A, Optics, image science, and vision.

[26]  David J. Fleet,et al.  Robust online appearance models for visual tracking , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[27]  Marc Parizeau,et al.  Training Hidden Markov Models with Multiple Observations-A Combinatorial Method , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  D. Field,et al.  Visual sensitivity, blur and the sources of variability in the amplitude spectra of natural scenes , 1997, Vision Research.

[29]  Chun-Hsien Chou,et al.  A perceptually tuned subband image coder based on the measure of just-noticeable-distortion profile , 1994, Proceedings of 1994 IEEE International Symposium on Information Theory.

[30]  Horst Bischof,et al.  Real-Time Tracking via On-line Boosting , 2006, BMVC.