Object tracking based on visual attention

Humans have the capability to quickly prioritize external visual stimuli and localize their most interest in a scene. Inspired by this mechanism, we propose a robust object tracking algorithm based on visual attention. We fuse motion feature and color feature to estimate the target state under the guidance of saliency map. Principal Component Analysis method is used to compute saliency feature based on the dense appearance model generated from the background templates. Motion feature is extracted by using the method which is a Bayesian decision rule for classification of background and foreground. Numerous experiments demonstrate the proposed method performs well against state-of-the-art tracking methods when dealing with illumination change, pose variation, occlusion, and background clutter situations.

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

[2]  Patrick Pérez,et al.  Probabilistic Color and Adaptive Multi-Feature Tracking with Dynamically Switched Priority Between Cues , 2007, 2007 IEEE 11th International Conference on Computer Vision.

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

[4]  Kuk-Jin Yoon,et al.  Visual Tracking via Adaptive Tracker Selection with Multiple Features , 2012, ECCV.

[5]  Luc Van Gool,et al.  An adaptive color-based particle filter , 2003, Image Vis. Comput..

[6]  Björn Stenger,et al.  Learning to track with multiple observers , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Zonghai Chen,et al.  Global feature integration based salient region detection , 2015, Neurocomputing.

[8]  Huchuan Lu,et al.  Robust object tracking via sparsity-based collaborative model , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[12]  Emilio Maggio,et al.  Adaptive Multifeature Tracking in a Particle Filtering Framework , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[13]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[14]  Qi Tian,et al.  Foreground object detection from videos containing complex background , 2003, MULTIMEDIA '03.

[15]  Luc Van Gool,et al.  Probabilistic object tracking using multiple features , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

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

[17]  Lu Zhang,et al.  Structure Preserving Object Tracking , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .