Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Fragments-based Tracking using the Integral Histogram
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
ACM Computing Surveys (CSUR)
Multiple Collaborative Kernel Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust object tracking with background-weighted local kernels
Computer Vision and Image Understanding
Tracking multiple people with recovery from partial and total occlusion
Pattern Recognition
Event Detection Using Trajectory Clustering and 4-D Histograms
IEEE Transactions on Circuits and Systems for Video Technology
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This paper presents a low-cost tracking algorithm based on multiple multiple fragments, increasing robustness with respect to partial occlusions. Given the initial template representing the desired target, each pixel is classified into a different cluster based on a Mixture of Gaussians (MOG) model, and a set of disjoint fragments is created. The mean vector and covariance matrix of each fragment are computed, and the Mahalanobis distance is used to decide which pixels of the adjacent frame within a neighborhood are associated with each fragment. The template is then placed at the position that maximizes a similarity measure based on the number of matched points.