@conference {12687,
	title = {Robust ego-motion estimation and 3D model refinement using depth based parallax model},
	booktitle = {Image Processing, 2004. ICIP {\textquoteright}04. 2004 International Conference on},
	volume = {4},
	year = {2004},
	month = {2004/10//},
	pages = {2483 - 2486 Vol. 4 - 2483 - 2486 Vol. 4},
	abstract = {We present an iterative algorithm for robustly estimating the ego-motion and refining and updating a coarse, noisy and partial depth map using a depth based parallax model and brightness derivatives extracted from an image pair. Given a coarse, noisy and partial depth map acquired by a range-finder or obtained from a Digital Elevation Map (DFM), we first estimate the ego-motion by combining a global ego-motion constraint and a local brightness constancy constraint. Using the estimated camera motion and the available depth map estimate, motion of the 3D points is compensated. We utilize the fact that the resulting surface parallax field is an epipolar field and knowing its direction from the previous motion estimates, estimate its magnitude and use it to refine the depth map estimate. Instead of assuming a smooth parallax field or locally smooth depth models, we locally model the parallax magnitude using the depth map, formulate the problem as a generalized eigen-value analysis and obtain better results. In addition, confidence measures for depth estimates are provided which can be used to remove regions with potentially incorrect (and outliers in) depth estimates for robustly estimating ego-motion in the next iteration. Results on both synthetic and real examples are presented.},
	keywords = {3D, algorithm;, analysis;, and, based, camera;, coarse, compensation;, DEM;, depth, digital, ego-motion, eigen-value, eigenfunctions;, eigenvalues, ELEVATION, epipolar, estimation;, extraction;, feature, field;, iteration, iterative, map;, method;, methods;, model, model;, MOTION, parallax, partial, range-finding;, refinement;, refining;, surface},
	doi = {10.1109/ICIP.2004.1421606},
	author = {Agrawala, Ashok K. and Chellapa, Rama}
}
@conference {12697,
	title = {View independent human body pose estimation from a single perspective image},
	booktitle = {Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on},
	volume = {2},
	year = {2004},
	month = {2004/07/02/june},
	pages = {II-16 - II-22 Vol.2 - II-16 - II-22 Vol.2},
	abstract = {Recovering the 3D coordinates of various joints of the human body from an image is a critical first step for several model-based human tracking and optical motion capture systems. Unlike previous approaches that have used a restrictive camera model or assumed a calibrated camera, our work deals with the general case of a perspective uncalibrated camera and is thus well suited for archived video. The input to the system is an image of the human body and correspondences of several body landmarks, while the output is the set of 3D coordinates of the landmarks in a body-centric coordinate system. Using ideas from 3D model based invariants, we set up a polynomial system of equations in the unknown head pitch, yaw and roll angles. If we are able to make the often-valid assumption that the torso twist is small, there are finite numbers of solutions to the head-orientation that can be computed readily. Once the head orientation is computed, the epipolar geometry of the camera is recovered, leading to solutions to the 3D joint positions. Results are presented on synthetic and real images.},
	keywords = {3D, analysis;, biomechanics;, body, body-centric, camera;, capture, coordinate, coordinates;, detection;, epipolar, equation, estimation;, geometry;, human, image, image;, images;, model-based, models;, MOTION, object, optical, perspective, physiological, polynomial, polynomials;, pose, real, single, synthetic, system;, systems;, torso, tracking;, twist;, uncalibrated},
	doi = {10.1109/CVPR.2004.1315139},
	author = {Parameswaran, V. and Chellapa, Rama}
}
@conference {13245,
	title = {Scalable image-based multi-camera visual surveillance system},
	booktitle = {Proceedings. IEEE Conference on Advanced Video and Signal Based Surveillance, 2003.},
	year = {2003},
	month = {2003/07//},
	pages = {205 - 212},
	abstract = {We describe the design of a scalable and wide coverage visual surveillance system. Scalability (the ability to add and remove cameras easily during system operation with minimal overhead and system degradation) is achieved by utilizing only image-based information for camera control. We show that when a pan-tilt-zoom camera pans and tilts, a given image point moves in a circular and a linear trajectory, respectively. We create a scene model using a plan view of the scene. The scene model makes it easy for us to handle occlusion prediction and schedule video acquisition tasks subject to visibility constraints. We describe a maximum weight matching algorithm to assign cameras to tasks that meet the visibility constraints. The system is illustrated both through simulations and real video from a 6-camera configuration.},
	keywords = {ACQUISITION, algorithm;, camera;, constraints;, feature, hidden, image-based, MATCHING, maximum, multi-camera, occlusion, pan-tilt-zoom, PLAN, prediction;, processing;, removal;, scalable, scheduling;, signal, Surveillance, surveillance;, system;, task, video, view;, visibility, visual, weight},
	doi = {10.1109/AVSS.2003.1217923},
	author = {Lim,Ser-Nam and Davis, Larry S. and Elgammal,A.}
}
@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}
}
@conference {12727,
	title = {Video based rendering of planar dynamic scenes},
	booktitle = {Multimedia and Expo, 2003. ICME {\textquoteright}03. Proceedings. 2003 International Conference on},
	volume = {1},
	year = {2003},
	month = {2003/07//},
	pages = {I - 477-80 vol.1 - I - 477-80 vol.1},
	abstract = {In this paper, we propose a method to synthesize arbitrary views of a planar scene from a monocular video sequence of it. The 3-D direction of motion of the object is robustly estimated from the video sequence. Given this direction any other view of the object can be synthesized through a perspective projection approach, under assumptions of planarity. If the distance of the object from the camera is large, a planar approximation is reasonable even for non-planar scenes. Such a method has many important applications, one of them being gait recognition where a side view of the person is required. Our method can be used to synthesize the side-view of the person in case he/she does not present a side view to the camera. Since the planarity assumption is often an approximation, the effects of non-planarity can lead to inaccuracies in rendering and needs to be corrected for. Regions where this happens are examined and a simple technique based on weak perspective approximation is proposed to offset rendering inaccuracies. Examples of synthesized views using our method and performance evaluation are presented.},
	keywords = {(computer, 3D, analysis;, approximation;, based, camera;, cameras;, direction;, dynamic, graphics);, image, monocular, MOTION, perspective, planar, processing;, rendering, rendering;, scenes;, sequence;, sequences;, signal, video, weak},
	doi = {10.1109/ICME.2003.1220958},
	author = {Kale, A. and Chowdhury, A.K.R. and Chellapa, Rama}
}
