Target Tracking Algorithm Based on Multi-Subblock Feature Matching

Real-time target tracking is an important subject in modern intelligent surveillance and security defense systems. However, due to the natural scene’s complexity and variability, the tracking becomes complex and difficult especially when the target is occluded in complex background. This paper proposes a tracking algorithm of moving target based on adaptive blocking and feature correlation matching. We compute target’s grayscale first, and judge the target’s grayscale attribute. Then according to it, we choose a more suitable algorithm to track moving target from edge correlation matching algorithm and grayscale correlation matching algorithm based on multi-subblock. To edge matching algorithm, target’s displacement in two successive frames is determined by optimal matching of current unoccluded edge with real-time updated target template. For grayscale correlation matching based on multi-subblocks, the algorithm first estimates occluded region accurately by subblocks with distinct feature, and then tracks the target by residual unoccluded subblocks to participate in grayscale correlation matching. The experimental results of our tracking system show that the algorithm is effective for tracking moving targets.

[1]  Gang Hua,et al.  Tracking appearances with occlusions , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[2]  Ken Ito,et al.  Robust view-based visual tracking with detection of occlusions , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[3]  Ioannis Pitas,et al.  Occlusion resistant object tracking , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

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

[5]  Katsushi Ikeuchi,et al.  Occlusion robust tracking utilizing spatio-temporal Markov random field model , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[6]  Peng Wang,et al.  An object tracking algorithm based on occlusion mesh model , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

[7]  Rashid Ansari,et al.  Kernel particle filter for visual tracking , 2005, IEEE Signal Processing Letters.

[8]  A. Murat Tekalp,et al.  Occlusion-adaptive, content-based mesh design and forward tracking , 1997, IEEE Trans. Image Process..

[9]  M. Worring,et al.  Occlusion robust adaptive template tracking , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[10]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[11]  Carlo S. Regazzoni,et al.  Multiple object tracking under heavy occlusions by using Kalman filters based on shape matching , 2002, Proceedings. International Conference on Image Processing.

[12]  Natan Peterfreund,et al.  Robust Tracking of Position and Velocity With Kalman Snakes , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  K. S. P. Kumar,et al.  A 'current' statistical model and adaptive algorithm for estimating maneuvering targets , 1984 .

[14]  Brendan McCane,et al.  Virtual snakes for occlusion analysis , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[15]  Takayuki Okatani,et al.  Object tracking by the mean-shift of regional color distribution combined with the particle-filter algorithms , 2004, ICPR 2004.

[16]  Tieniu Tan,et al.  Real time hand tracking by combining particle filtering and mean shift , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[17]  Helman Stern,et al.  Adaptive color space switching for tracking under varying illumination , 2005, Image Vis. Comput..

[18]  Franco Oberti,et al.  Robust tracking of humans and vehicles in cluttered scenes with occlusions , 2002, Proceedings. International Conference on Image Processing.