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.
© 2012 Optical Society of America
Original Manuscript: December 8, 2011
Revised Manuscript: April 24, 2012
Manuscript Accepted: May 23, 2012
Published: July 11, 2012
Zhile Wang, Qingyu Hou, and Ling Hao, "Improved infrared target-tracking algorithm based on mean shift," Appl. Opt. 51, 5051-5059 (2012)