@conference {13140,
	title = {Probabilistic Fusion Tracking Using Mixture Kernel-Based Bayesian Filtering},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {Even though sensor fusion techniques based on particle filters have been applied to object tracking, their implementations have been limited to combining measurements from multiple sensors by the simple product of individual likelihoods. Therefore, the number of observations is increased as many times as the number of sensors, and the combined observation may become unreliable through blind integration of sensor observations - especially if some sensors are too noisy and non-discriminative. We describe a methodology to model interactions between multiple sensors and to estimate the current state by using a mixture of Bayesian filters - one filter for each sensor, where each filter makes a different level of contribution to estimate the combined posterior in a reliable manner. In this framework, an adaptive particle arrangement system is constructed in which each particle is allocated to only one of the sensors for observation and a different number of samples is assigned to each sensor using prior distribution and partial observations. We apply this technique to visual tracking in logical and physical sensor fusion frameworks, and demonstrate its effectiveness through tracking results.},
	keywords = {(numerical, adaptive, arrangement, Bayesian, Filtering, filtering;multiple, filters;probabilistic, fusion, fusion;tracking;, integration;mixture, kernel-based, methods);sensor, methods;array, particle, processing;particle, sensors;object, signal, system;blind, techniques;visual, tracking;Bayes, tracking;particle, tracking;sensor},
	doi = {10.1109/ICCV.2007.4408938},
	author = {Han,Bohyung and Joo,Seong-Wook and Davis, Larry S.}
}
@conference {13198,
	title = {Kernel-based Bayesian filtering for object tracking},
	booktitle = {Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on},
	volume = {1},
	year = {2005},
	month = {2005/06//},
	pages = {227 - 234 vol. 1 - 227 - 234 vol. 1},
	abstract = {Particle filtering provides a general framework for propagating probability density functions in nonlinear and non-Gaussian systems. However, the algorithm is based on a Monte Carlo approach and sampling is a problematic issue, especially for high dimensional problems. This paper presents a new kernel-based Bayesian filtering framework, which adopts an analytic approach to better approximate and propagate density functions. In this framework, the techniques of density interpolation and density approximation are introduced to represent the likelihood and the posterior densities by Gaussian mixtures, where all parameters such as the number of mixands, their weight, mean, and covariance are automatically determined. The proposed analytic approach is shown to perform sampling more efficiently in high dimensional space. We apply our algorithm to real-time tracking problems, and demonstrate its performance on real video sequences as well as synthetic examples.},
	keywords = {approach;, approximation;, Bayes, Bayesian, Carlo, density, detection;, filtering;, functions;, Gaussian, interpolation;, kernel-based, methods;, mixtures;, Monte, nonGaussian, nonlinear, object, particle, probability, probability;, processes;, recognition;, sampling, sampling;, sequences;, system;, tracking;, video},
	doi = {10.1109/CVPR.2005.199},
	author = {Han,Bohyung and Zhu,Ying and Comaniciu, D. and Davis, Larry S.}
}
@conference {13190,
	title = {Segmentation and appearance model building from an image sequence},
	booktitle = {Image Processing, 2005. ICIP 2005. IEEE International Conference on},
	volume = {1},
	year = {2005},
	month = {2005/09//},
	pages = {I - 321-4 - I - 321-4},
	abstract = {In this paper we explore the problem of accurately segmenting a person from a video given only approximate location of that person. Unlike previous work which assumes that the appearance model is known in advance, we developed an iterative expectation-sampling (ES) algorithm for solving segmentation and appearance modeling simultaneously The appearance model is encoded with a kernel-based PDF defined in a joint color/path-length space. This appearance model remains unchanged during a short time period, although the object can articulate. Thus, we can perform the ES iteration not only for a single frame but also for an image sequence. The algorithm is iterative, but simple, efficient and gives visually good results.},
	keywords = {algorithm;, color-path-length, expectation-sampling, image, iterative, joint, kernel-based, methods;, PDF;, person, segmentation;, sequence;, sequences;, space;},
	doi = {10.1109/ICIP.2005.1529752},
	author = {Liang Zhao and Davis, Larry S.}
}
