@article {13107,
	title = {Visual Tracking by Continuous Density Propagation in Sequential Bayesian Filtering Framework},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {31},
	year = {2009},
	month = {2009/05//},
	pages = {919 - 930},
	abstract = {Particle filtering is frequently used for visual tracking problems since it provides a general framework for estimating and propagating probability density functions for nonlinear and non-Gaussian dynamic systems. However, this algorithm is based on a Monte Carlo approach and the cost of sampling and measurement is a problematic issue, especially for high-dimensional problems. We describe an alternative to the classical particle filter in which the underlying density function has an analytic representation for better approximation and effective propagation. The techniques of density interpolation and density approximation are introduced to represent the likelihood and the posterior densities with Gaussian mixtures, where all relevant parameters are automatically determined. The proposed analytic approach is shown to perform more efficiently in sampling in high-dimensional space. We apply the algorithm to real-time tracking problems and demonstrate its performance on real video sequences as well as synthetic examples.},
	keywords = {Automated;Reproducibility of Results;Sensitivity and Specificity;Signal Processing, Computer-Assisted;Pattern Recognition, Computer-Assisted;Subtraction Technique;, Monte Carlo approach;continuous density propagation;density approximation;density interpolation;nonGaussian dynamic systems;nonlinear dynamic systems;particle filtering;probability density functions;sequential Bayesian filtering framework;video sequences;},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2008.134},
	author = {Han,Bohyung and Zhu,Ying and Comaniciu, D. and Davis, Larry S.}
}
@article {12571,
	title = {A Multiple-Hypothesis Approach for Multiobject Visual Tracking},
	journal = {Image Processing, IEEE Transactions on},
	volume = {16},
	year = {2007},
	month = {2007/11//},
	pages = {2849 - 2854},
	abstract = {In multiple-object tracking applications, it is essential to address the problem of associating targets and observation data. For visual tracking of multiple targets which involves objects that split and merge, a target may be associated with multiple measurements and many targets may be associated with a single measurement. The space of such data association is exponential in the number of targets and exhaustive enumeration is impractical. We pose the association problem as a bipartite graph edge covering problem given the targets and the object detection information. We propose an efficient method of maintaining multiple association hypotheses with the highest probabilities over all possible histories of associations. Our approach handles objects entering and exiting the field of view, merging and splitting objects, as well as objects that are detected as fragmented parts. Experimental results are given for tracking multiple players in a soccer game and for tracking people with complex interaction in a surveillance setting. It is shown through quantitative evaluation that our method tracks through varying degrees of interactions among the targets with high success rate.},
	keywords = {Automated;Reproducibility of Results;Sensitivity and Specificity;Signal Processing, bipartite graph edge covering problem;data association;multiobject visual tracking;multiple measurements;multiple-hypothesis approach;multiple-object tracking;object detection;graph theory;object detection;probability;target tracking;Algorithms;Artificial, Computer-Assisted;Motion;Numerical Analysis, Computer-Assisted;Pattern Recognition, Computer-Assisted;Subtraction Technique;},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2007.906254},
	author = {Joo,Seong-Wook and Chellapa, Rama}
}
@article {12656,
	title = {Background learning for robust face recognition with PCA in the presence of clutter},
	journal = {Image Processing, IEEE Transactions on},
	volume = {14},
	year = {2005},
	month = {2005/06//},
	pages = {832 - 843},
	abstract = {We propose a new method within the framework of principal component analysis (PCA) to robustly recognize faces in the presence of clutter. The traditional eigenface recognition (EFR) method, which is based on PCA, works quite well when the input test patterns are faces. However, when confronted with the more general task of recognizing faces appearing against a background, the performance of the EFR method can be quite poor. It may miss faces completely or may wrongly associate many of the background image patterns to faces in the training set. In order to improve performance in the presence of background, we argue in favor of learning the distribution of background patterns and show how this can be done for a given test image. An eigenbackground space is constructed corresponding to the given test image and this space in conjunction with the eigenface space is used to impart robustness. A suitable classifier is derived to distinguish nonface patterns from faces. When tested on images depicting face recognition in real situations against cluttered background, the performance of the proposed method is quite good with fewer false alarms.},
	keywords = {Automated;Principal Component Analysis;Reproducibility of Results;Sensitivity and Specificity;Signal Processing, Biological;Models, Computer-Assisted;Information Storage and Retrieval;Models, Computer-Assisted;Subtraction Technique;, PCA;background learning;clutter;eigenface recognition method;face recognition;linear discriminant;principal component analysis;clutter;eigenvalues and eigenfunctions;face recognition;learning (artificial intelligence);principal component analysis;Algorith, Statistical;Pattern Recognition},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2005.847288},
	author = {Rajagopalan, AN and Chellapa, Rama and Koterba, N.T.}
}
