Improved infrared target-tracking algorithm based on mean shift.

An improved IR target-tracking algorithm based on mean shift is proposed herein, which combines the mean-shift-based gradient-matched searching strategy with a feature-classification-based tracking algorithm. An improved target representation model is constructed by considering the likelihood ratio of the gray-level features of the target and local background as a weighted value of the original kernel histogram of the target region. An expression for the mean-shift vector in this model is derived, and a criterion for updating the model is presented. Experimental results show that the algorithm improves the shift weight of the target pixel gray level and suppresses background disturbance.

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

[2]  Khan M. Iftekharuddin,et al.  Automated tracking and classification of infrared images , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[3]  Emilio Maggio,et al.  Hybrid particle filter and mean shift tracker with adaptive transition model , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[4]  Yasushi Yagi,et al.  Integrating Color and Shape-Texture Features for Adaptive Real-Time Object Tracking , 2008, IEEE Transactions on Image Processing.

[5]  Qiao Shuang.  Anti-occlusion arithmetic for moving object tracking , 2007 .

[6]  Shai Avidan.  Ensemble Tracking , 2007, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Yanxi Liu,et al.  Online Selection of Discriminative Tracking Features , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  David Suter,et al.  Adaptive Object Tracking Based on an Effective Appearance Filter , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Mubarak Shah,et al.  Target tracking in airborne forward looking infrared imagery , 2003, Image Vis. Comput..

[10]  Tyng-Luh Liu,et al.  Probabilistic tracking with adaptive feature selection , 2004, ICPR 2004.

[11]  Robert T. Collins,et al.  Spatial Divide and Conquer with Motion Cues for Tracking through Clutter , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[12]  Salah Bourennane,et al.  Pearson-based mixture model for color object tracking , 2008, Machine Vision and Applications.

[13]  Li Zhang,et al.  Online Feature Extraction and Selection for Object Tracking , 2007, 2007 International Conference on Mechatronics and Automation.

[14]  Ren Ming.  NEW INFRARED OBJECT TRACKING ALGORITHM BASED ON SMOG MODEL , 2008 .