@conference {17790,
	title = {RDF aggregate queries and views},
	booktitle = {Data Engineering, 2005. ICDE 2005. Proceedings. 21st International Conference on},
	year = {2005},
	month = {2005/04//},
	pages = {717 - 728},
	abstract = {Resource description framework (RDF) is a rapidly expanding Web standard. RDF databases attempt to track the massive amounts of Web data and services available. In this paper, we study the problem of aggregate queries. We develop an algorithm to compute answers to aggregate queries over RDF databases and algorithms to maintain views involving those aggregates. Though RDF data can be stored in a standard relational DBMS (and hence we can execute standard relational aggregate queries and view maintenance methods on them), we show experimentally that our algorithms that operate directly on the RDF representation exhibit significantly superior performance.},
	keywords = {aggregate, databases;, DBMS;, description, framework;, languages;, Maintenance, methods;, processing;, queries;, query, RDF, relational, resource, standard;, standards;, view, Web},
	doi = {10.1109/ICDE.2005.121},
	author = {Hung,E. and Deng,Yu and V.S. Subrahmanian}
}
@conference {13193,
	title = {Robust observations for object tracking},
	booktitle = {Image Processing, 2005. ICIP 2005. IEEE International Conference on},
	volume = {2},
	year = {2005},
	month = {2005/09//},
	pages = {II - 442-5 - II - 442-5},
	abstract = {It is a difficult task to find an observation model that will perform well for long-term visual tracking. In this paper, we propose an adaptive observation enhancement technique based on likelihood images, which are derived from multiple visual features. The most discriminative likelihood image is extracted by principal component analysis (PCA) and incrementally updated frame by frame to reduce temporal tracking error. In the particle filter framework, the feasibility of each sample is computed using this most discriminative likelihood image before the observation process. Integral image is employed for efficient computation of the feasibility of each sample. We illustrate how our enhancement technique contributes to more robust observations through demonstrations.},
	keywords = {(numerical, adaptive, analysis;, component, enhancement;, filter, Filtering, framework;, image, images;, likelihood, methods);, object, observation, particle, PCA;, principal, tracking;},
	doi = {10.1109/ICIP.2005.1530087},
	author = {Han,Bohyung and Davis, Larry S.}
}
@conference {12702,
	title = {Robust two-camera tracking using homography},
	booktitle = {Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP {\textquoteright}04). IEEE International Conference on},
	volume = {3},
	year = {2004},
	month = {2004/05//},
	pages = {iii - 1-4 vol.3 - iii - 1-4 vol.3},
	abstract = {The paper introduces a two view tracking method which uses the homography relation between the two views to handle occlusions. An adaptive appearance-based model is incorporated in a particle filter to realize robust visual tracking. Occlusion is detected using robust statistics. When there is occlusion in one view, the homography from this view to other views is estimated from previous tracking results and used to infer the correct transformation for the occluded view. Experimental results show the robustness of the two view tracker.},
	keywords = {Carlo, filter;, filters;, frame, framework;, homography;, image, method;, methods;, Monte, nonlinear, occlusions;, optical, particle, processing;, robust, sequences;, sequential, signal, statistics;, tracking, tracking;, two, two-camera, video, view, visual},
	doi = {10.1109/ICASSP.2004.1326466},
	author = {Yue,Zhanfeng and Zhou,S. K and Chellapa, Rama}
}
@conference {12705,
	title = {Vehicle detection and tracking using acoustic and video sensors},
	booktitle = {Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP {\textquoteright}04). IEEE International Conference on},
	volume = {3},
	year = {2004},
	month = {2004/05//},
	pages = {iii - 793-6 vol.3 - iii - 793-6 vol.3},
	abstract = {Multimodal sensing has attracted much attention in solving a wide range of problems, including target detection, tracking, classification, activity understanding, speech recognition, etc. In surveillance applications, different types of sensors, such as video and acoustic sensors, provide distinct observations of ongoing activities. We present a fusion framework using both video and acoustic sensors for vehicle detection and tracking. In the detection phase, a rough estimate of target direction-of-arrival (DOA) is first obtained using acoustic data through beam-forming techniques. This initial DOA estimate designates the approximate target location in video. Given the initial target position, the DOA is refined by moving target detection using the video data. Markov chain Monte Carlo techniques are then used for joint audio-visual tracking. A novel fusion approach has been proposed for tracking, based on different characteristics of audio and visual trackers. Experimental results using both synthetic and real data are presented. Improved tracking performance has been observed by fusing the empirical posterior probability density functions obtained using both types of sensors.},
	keywords = {acoustic, applications;, audio, audio-visual, beam-forming, Carlo, chain, density, detection;, direction-of-arrival, DOA, empirical, estimation;, framework;, functions;, fusion, fusion;, joint, Markov, methods;, Monte, moving, multimodal, object, optical, posterior, probability, probability;, processes;, processing;, sensing;, sensor, sensors;, signal, Surveillance, surveillance;, systems;, target, techniques;, tracking;, vehicle, video},
	doi = {10.1109/ICASSP.2004.1326664},
	author = {Chellapa, Rama and Qian,Gang and Qinfen Zheng}
}
@conference {13240,
	title = {Exemplar-based tracking and recognition of arm gestures},
	booktitle = {Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the 3rd International Symposium on},
	volume = {2},
	year = {2003},
	month = {2003/09//},
	pages = {656 - 661 Vol.2 - 656 - 661 Vol.2},
	abstract = {This paper presents a probabilistic exemplar-based framework for recognizing gestures. The approach is based on representing each gesture as a sequence of learned body poses. The gestures are recognized through a probabilistic framework for matching these body poses and for imposing temporal constrains between different poses. Matching individual poses to image data is performed using a probabilistic formulation for edge matching to obtain a likelihood measurement for each individual pose. The paper introduces a correspondence-free weighted matching scheme for edge templates that emphasize discriminating features in the matching. The weighting does not require establishing correspondences between the different pose models. The probabilistic framework also imposes temporal constrains between different pose through a learned hidden Markov model (HMM) of each gesture.},
	keywords = {arm, constrains;, correspondence-free, edge, exemplar-based, framework;, gesture, hidden, HMM;, image, logic;, Markov, MATCHING, matching;, model;, models;, probabilistic, recognition;, scheme;, segmentation;, temporal, tracking;, weighted},
	doi = {10.1109/ISPA.2003.1296358},
	author = {Elgammal,A. and Shet,V. and Yacoob,Yaser and Davis, Larry S.}
}
@conference {13247,
	title = {Learning dynamics for exemplar-based gesture recognition},
	booktitle = {Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on},
	volume = {1},
	year = {2003},
	month = {2003/06//},
	pages = {I-571 - I-578 vol.1 - I-571 - I-578 vol.1},
	abstract = {This paper addresses the problem of capturing the dynamics for exemplar-based recognition systems. Traditional HMM provides a probabilistic tool to capture system dynamics and in exemplar paradigm, HMM states are typically coupled with the exemplars. Alternatively, we propose a non-parametric HMM approach that uses a discrete HMM with arbitrary states (decoupled from exemplars) to capture the dynamics over a large exemplar space where a nonparametric estimation approach is used to model the exemplar distribution. This reduces the need for lengthy and non-optimal training of the HMM observation model. We used the proposed approach for view-based recognition of gestures. The approach is based on representing each gesture as a sequence of learned body poses (exemplars). The gestures are recognized through a probabilistic framework for matching these body poses and for imposing temporal constraints between different poses using the proposed non-parametric HMM.},
	keywords = {arbitrary, body, by, Computer, constraint;, detection;, discrete, distribution, dynamics;, edge, estimation;, example;, exemplar, exemplar-based, extraction;, feature, framework;, gesture, gesture;, hidden, HMM;, human, image, learning, Markov, matching;, model;, models;, motion;, nonparametric, pose, probabilistic, recognition;, sequence;, space;, state;, statistics;, system, temporal, tool;, view-based, vision;},
	doi = {10.1109/CVPR.2003.1211405},
	author = {Elgammal,A. and Shet,V. and Yacoob,Yaser and Davis, Larry S.}
}
@conference {12725,
	title = {Shape and motion driven particle filtering for human body tracking},
	booktitle = {Multimedia and Expo, 2003. ICME {\textquoteright}03. Proceedings. 2003 International Conference on},
	volume = {3},
	year = {2003},
	month = {2003/07//},
	pages = {III - 61-4 vol.3 - III - 61-4 vol.3},
	abstract = {In this paper, we propose a method to recover 3D human body motion from a video acquired by a single static camera. In order to estimate the complex state distribution of a human body, we adopt the particle filtering framework. We present the human body using several layers of representation and compose the whole body step by step. In this way, more effective particles are generated and ineffective particles are removed as we process each layer. In order to deal with the rotational motion, the frequency of rotation is obtained using a preprocessing operation. In the preprocessing step, the variance of the motion field at each image is computed, and the frequency of rotation is estimated. The estimated frequency is used for the state update in the algorithm. We successfully track the movement of figure skaters in TV broadcast image sequence, and recover the 3D shape and motion of the skater.},
	keywords = {3D, body, broadcast, camera;, cameras;, estimation;, Filtering, framework;, human, image, MOTION, motion;, particle, processing;, rotational, sequence;, sequences;, signal, single, static, theory;, tracking;, TV, video},
	doi = {10.1109/ICME.2003.1221248},
	author = {Yamamoto, T. and Chellapa, Rama}
}
