Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time closed-world tracking
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Robust human tracking based on multi-cue integration and mean-shift
Pattern Recognition Letters
Adaptive pyramid mean shift for global real-time visual tracking
Image and Vision Computing
Mean Shift tracking with multiple reference color histograms
Computer Vision and Image Understanding
A constrained optimization approach for an adaptive generalized subspace tracking algorithm
Computers and Electrical Engineering
Segmenting and tracking multiple objects under occlusion using multi-label graph cut
Computers and Electrical Engineering
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
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Color feature is now taken into real consideration as one of the important cues in the area of objects tracking, in image sequences. This feature has attracted considerable attention, in recent years. One of the well-known tools in color feature extraction is to use mean shift (MS) tracking algorithm. The probability of finding the object location in line with this tracking algorithm is somehow desirable, in image sequences, by maximizing the Bhattacharyya coefficient between both objects and corresponding candidate models. Even though the MS tracking algorithm is just known as a popular tool in the field of object tracking, it does not have sufficient merit to be realized in complex environments, i.e., background with object's similar color, sudden light changes, occlusion types and so on. In such a case, the amount of the present coefficient could truly be decreased, during the tracking process, because of the mentioned environmental problems. A convex kernel function in association with the motion information of video sequences is used in this investigation to improve the MS tracking algorithm for the purpose of overcoming the existing problems. The proposed approach is employed to present the MS kernel function, directly. Thus, by using the investigation in its present form, the capability of the MS kernel is increased. Moreover, by using both color feature and motion information, simultaneously, in comparison with single color feature, noises and also uninterested regions can actually be eliminated. Experimental results on data set illustrate that the proposed approach has an optimum performance in real-time object tracking under the severe conditions.