@conference {12704,
	title = {Fusion of gait and face for human identification},
	booktitle = {Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP {\textquoteright}04). IEEE International Conference on},
	volume = {5},
	year = {2004},
	month = {2004/05//},
	pages = {V - 901-4 vol.5 - V - 901-4 vol.5},
	abstract = {Identification of humans from arbitrary view points is an important requirement for different tasks including perceptual interfaces for intelligent environments, covert security and access control etc. For optimal performance, the system must use as many cues as possible and combine them in meaningful ways. In this paper, we discuss fusion of face and gait cues for the single camera case. We present a view invariant gait recognition algorithm for gait recognition. We employ decision fusion to combine the results of our gait recognition algorithm and a face recognition algorithm based on sequential importance sampling. We consider two fusion scenarios: hierarchical and holistic. The first involves using the gait recognition algorithm as a filter to pass on a smaller set of candidates to the face recognition algorithm. The second involves combining the similarity scores obtained individually from the face and gait recognition algorithms. Simple rules like the SUM, MIN and PRODUCT are used for combining the scores. The results of fusion experiments are demonstrated on the NIST database which has outdoor gait and face data of 30 subjects.},
	keywords = {access, algorithm;, analysis;, combining, control;, covert, cues;, data, decision, Environment, Face, fusion;, Gait, hierarchical, holistic, human, identification;, importance, intelligent, interfaces;, invariant, perceptual, recognition, recognition;, rules;, sampling;, score, scores;, security;, sensor, sequential, similarity, view},
	doi = {10.1109/ICASSP.2004.1327257},
	author = {Kale, A. and Roy Chowdhury, A.K. and Chellapa, Rama}
}
@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 {12758,
	title = {Bayesian structure from motion using inertial information},
	booktitle = {Image Processing. 2002. Proceedings. 2002 International Conference on},
	volume = {3},
	year = {2002},
	month = {2002///},
	pages = {III-425 - III-428 vol.3 - III-425 - III-428 vol.3},
	abstract = {A novel approach to Bayesian structure from motion (SfM) using inertial information and sequential importance sampling (SIS) is presented. The inertial information is obtained from camera-mounted inertial sensors and is used in the Bayesian SfM approach as prior knowledge of the camera motion in the sampling algorithm. Experimental results using both synthetic and real images show that, when inertial information is used, more accurate results can be obtained or the same estimation accuracy can be obtained at a lower cost.},
	keywords = {3D, analysis;, Bayes, Bayesian, camera, estimation;, image, images;, importance, inertial, information;, methods;, MOTION, motion;, parameter, processing;, real, reconstruction;, sampling;, scene, sensors;, sequence;, sequences;, sequential, signal, structure-from-motion;, synthetic, systems;, video},
	doi = {10.1109/ICIP.2002.1038996},
	author = {Qian,Gang and Chellapa, Rama and Qinfen Zheng}
}
@article {12752,
	title = {A generic approach to simultaneous tracking and verification in video},
	journal = {Image Processing, IEEE Transactions on},
	volume = {11},
	year = {2002},
	month = {2002/05//},
	pages = {530 - 544},
	abstract = {A generic approach to simultaneous tracking and verification in video data is presented. The approach is based on posterior density estimation using sequential Monte Carlo methods. Visual tracking, which is in essence a temporal correspondence problem, is solved through probability density propagation, with the density being defined over a proper state space characterizing the object configuration. Verification is realized through hypothesis testing using the estimated posterior density. In its most basic form, verification can be performed as follows. Given a measurement vector Z and two hypotheses H<sub>1</sub> and H0, we first estimate posterior probabilities P(H<sub>0</sub>|Z) and P(H<sub>1</sub>|Z), and then choose the one with the larger posterior probability as the true hypothesis. Several applications of the approach are illustrated by experiments devised to evaluate its performance. The idea is first tested on synthetic data, and then experiments with real video sequences are presented, illustrating vehicle tracking and verification, human (face) tracking and verification, facial feature tracking, and image sequence stabilization.},
	keywords = {approach;, Carlo, configuration;, correspondence, data;, density, density;, estimated, estimation;, evaluation;, extraction;, Face, facial, feature, generic, human, hypothesis, image, measurement, methods;, Monte, object, performance, posterior, probability, probability;, problem;, processing;, propagation;, recognition;, road, sequence, sequences;, sequential, signal, space;, stabilization;, state, synthetic, temporal, testing;, tracking;, vector;, vehicle, vehicles;, verification;, video, visual},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2002.1006400},
	author = {Li,Baoxin and Chellapa, Rama}
}
@conference {12766,
	title = {Probabilistic recognition of human faces from video},
	booktitle = {Image Processing. 2002. Proceedings. 2002 International Conference on},
	volume = {1},
	year = {2002},
	month = {2002///},
	pages = {I-41 - I-44 vol.1 - I-41 - I-44 vol.1},
	abstract = {Most present face recognition approaches recognize faces based on still images. We present a novel approach to recognize faces in video. In that scenario, the face gallery may consist of still images or may be derived from a videos. For evidence integration we use classical Bayesian propagation over time and compute the posterior distribution using sequential importance sampling. The probabilistic approach allows us to handle uncertainties in a systematic manner. Experimental results using videos collected by NIST/USF and CMU illustrate the effectiveness of this approach in both still-to-video and video-to-video scenarios with appropriate model choices.},
	keywords = {Bayes, Bayesian, CMU;, distribution;, Face, faces;, gallery;, handling;, human, image, images;, importance, likelihood;, methods;, NIST/USF;, observation, posterior, probabilistic, probability;, processing;, propagation;, recognition;, sampling;, sequential, signal, still, Still-to-video, Uncertainty, video, Video-to-video},
	doi = {10.1109/ICIP.2002.1037954},
	author = {Chellapa, Rama and Kruger, V. and Zhou,Shaohua}
}
@conference {12756,
	title = {A robust algorithm for probabilistic human recognition from video},
	booktitle = {Pattern Recognition, 2002. Proceedings. 16th International Conference on},
	volume = {1},
	year = {2002},
	month = {2002///},
	pages = {226 - 229 vol.1 - 226 - 229 vol.1},
	abstract = {Human recognition from video requires solving the two tasks, recognition and tracking, simultaneously. This leads to a parameterized time series state space model, representing both motion and identity of the human. Sequential Monte Carlo (SMC) algorithms, like Condensation, can be developed to offer numerical solutions to this model. However in outdoor environments, the solution is more likely to diverge from the foreground, causing failures in both recognition and tracking. In this paper we propose an approach for tackling this problem by incorporating the constraint of temporal continuity in the observations. Experimental results demonstrate improvements over its Condensation counterpart.},
	keywords = {algorithm;, algorithms;, Carlo, continuity;, human, image, methods;, model;, Monte, parameterized, probabilistic, recognition;, robust, sequential, series, series;, space, state, state-space, temporal, TIME},
	doi = {10.1109/ICPR.2002.1044661},
	author = {Zhou,Shaohua and Chellapa, Rama}
}
