@article {12604,
	title = {Structure From Planar Motion},
	journal = {Image Processing, IEEE Transactions on},
	volume = {15},
	year = {2006},
	month = {2006/11//},
	pages = {3466 - 3477},
	abstract = {Planar motion is arguably the most dominant type of motion in surveillance videos. The constraints on motion lead to a simplified factorization method for structure from planar motion when using a stationary perspective camera. Compared with methods for general motion , our approach has two major advantages: a measurement matrix that fully exploits the motion constraints is formed such that the new measurement matrix has a rank of at most 3, instead of 4; the measurement matrix needs similar scalings, but the estimation of fundamental matrices or epipoles is not needed. Experimental results show that the algorithm is accurate and fairly robust to noise and inaccurate calibration. As the new measurement matrix is a nonlinear function of the observed variables, a different method is introduced to deal with the directional uncertainty in the observed variables. Differences and the dual relationship between planar motion and planar object are also clarified. Based on our method, a fully automated vehicle reconstruction system has been designed},
	keywords = {algebra;road, analysis;matrix, camera;surveillance, directional, matrix;planar, MOTION, motion;stationary, perspective, processing;, reconstruction, signal, system;image, uncertainty;measurement, vehicles;surveillance;video, videos;vehicle},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2006.881943},
	author = {Li,Jian and Chellapa, Rama}
}
@conference {12703,
	title = {Appearance-based tracking and recognition using the 3D trilinear tensor},
	booktitle = {Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP {\textquoteright}04). IEEE International Conference on},
	volume = {3},
	year = {2004},
	month = {2004/05//},
	pages = {iii - 613-16 vol.3 - iii - 613-16 vol.3},
	abstract = {The paper presents an appearance-based adaptive algorithm for simultaneous tracking and recognition by generalizing the transformation model to 3D perspective transformation. A trilinear tensor operator is used to represent the 3D geometrical structure. The tensor is estimated by predicting the corresponding points using the existing affine-transformation based algorithm. The estimated tensor is used to synthesize novel views to update the appearance templates. Some experimental results using airborne video are presented.},
	keywords = {3D, adaptive, affine-transformation, airborne, algorithm;, appearance, appearance-based, based, estimation;, geometrical, image, mathematical, novel, object, operator;, operators;, perspective, prediction;, processing;, recognition;, representation;, signal, structure, synthesis;, template, tensor, tensor;, tensors;, tracking;, transformation;, trilinear, updating;, video, video-based, video;, view},
	doi = {10.1109/ICASSP.2004.1326619},
	author = {Jie Shao and Zhou,S. K and Chellapa, Rama}
}
@conference {12683,
	title = {Multiple view tracking of humans modelled by kinematic chains},
	booktitle = {Image Processing, 2004. ICIP {\textquoteright}04. 2004 International Conference on},
	volume = {2},
	year = {2004},
	month = {2004/10//},
	pages = {1009 - 1012 Vol.2 - 1009 - 1012 Vol.2},
	abstract = {We use a kinematic chain to model human body motion. We estimate the kinematic chain motion parameters using pixel displacements calculated from video sequences obtained from multiple calibrated cameras to perform tracking. We derive a linear relation between the 2D motion of pixels in terms of the 3D motion parameters of various body parts using a perspective projection model for the cameras, a rigid body motion model for the base body and the kinematic chain model for the body parts. An error analysis of the estimator is provided, leading to an iterative algorithm for calculating the motion parameters from the pixel displacements. We provide experimental results to demonstrate the accuracy of our formulation. We also compare our iterative algorithm to the noniterative algorithm and discuss its robustness in the presence of noise.},
	keywords = {3D, algorithm;, analysis;, body, calibrated, cameras;, chain, displacement;, error, estimation;, human, image, iterative, kinematic, kinematics;, methods;, model;, MOTION, motion;, multiple, parameters;, perspective, Pixel, processing;, projection, sequences;, signal, tracking;, video, view},
	doi = {10.1109/ICIP.2004.1419472},
	author = {Sundaresan, A. and Chellapa, Rama and RoyChowdhury, R.}
}
@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 {16896,
	title = {Simultaneous pose and correspondence determination using line features},
	booktitle = {Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on},
	volume = {2},
	year = {2003},
	month = {2003/06//},
	pages = {II-424 - II-431 vol.2 - II-424 - II-431 vol.2},
	abstract = {We present a new robust line matching algorithm for solving the model-to-image registration problem. Given a model consisting of 3D lines and a cluttered perspective image of this model, the algorithm simultaneously estimates the pose of the model and the correspondences of model lines to image lines. The algorithm combines softassign for determining correspondences and POSIT for determining pose. Integrating these algorithms into a deterministic annealing procedure allows the correspondence and pose to evolve from initially uncertain values to a joint local optimum. This research extends to line features the SoftPOSIT algorithm proposed recently for point features. Lines detected in images are typically more stable than points and are less likely to be produced by clutter and noise, especially in man-made environments. Experiments on synthetic and real imagery with high levels of clutter, occlusion, and noise demonstrate the robustness of the algorithm.},
	keywords = {algorithm;, algorithms;, annealing;, clutter;, cluttered, Computer, correspondence, detection;, determination;, deterministic, environment;, extraction;, feature, feature;, image, image;, imagery;, images;, joint, line, local, man-made, MATCHING, matching;, measurement;, model-to-image, noise;, occlusion;, optimum;, perspective, point, pose, position, problem;, processing;, real, realistic, registration, simulated, softassign;, SoftPOSIT, stereo, synthetic, vision;},
	doi = {10.1109/CVPR.2003.1211499},
	author = {David,P. and DeMenthon,D. and Duraiswami, Ramani and Samet, Hanan}
}
@conference {12728,
	title = {Towards a view invariant gait recognition algorithm},
	booktitle = {Proceedings. IEEE Conference on Advanced Video and Signal Based Surveillance, 2003.},
	year = {2003},
	month = {2003/07//},
	pages = {143 - 150},
	abstract = {Human gait is a spatio-temporal phenomenon and typifies the motion characteristics of an individual. The gait of a person is easily recognizable when extracted from a side-view of the person. Accordingly, gait-recognition algorithms work best when presented with images where the person walks parallel to the camera image plane. However, it is not realistic to expect this assumption to be valid in most real-life scenarios. Hence, it is important to develop methods whereby the side-view can be generated from any other arbitrary view in a simple, yet accurate, manner. This is the main theme of the paper. We show that if the person is far enough from the camera, it is possible to synthesize a side view (referred to as canonical view) from any other arbitrary view using a single camera. Two methods are proposed for doing this: (i) using the perspective projection model; (ii) using the optical flow based structure from motion equations. A simple camera calibration scheme for this method is also proposed. Examples of synthesized views are presented. Preliminary testing with gait recognition algorithms gives encouraging results. A by-product of this method is a simple algorithm for synthesizing novel views of a planar scene.},
	keywords = {(access, algorithm;, analysis;, Biometrics, biometrics;, Calibration, calibration;, camera, canonical, control);, equations;, flow;, Gait, gait;, human, image, invariant, model;, MOTION, optical, perspective, phenomenon;, projection, recognition, scheme;, sequences;, spatio-temporal, view, view;},
	doi = {10.1109/AVSS.2003.1217914},
	author = {Kale, A. and Chowdhury, A.K.R. 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}
}
@conference {12735,
	title = {Video synthesis of arbitrary views for approximately planar scenes},
	booktitle = {Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP {\textquoteright}03). 2003 IEEE International Conference on},
	volume = {3},
	year = {2003},
	month = {2003/04//},
	pages = {III - 497-500 vol.3 - III - 497-500 vol.3},
	abstract = {In this paper, we propose a method to synthesize arbitrary views of a planar scene, given a monocular video sequence. The method is based on the availability of knowledge of the angle between the original and synthesized views. Such a method has many important applications, one of them being gait recognition. Gait recognition algorithms rely on the availability of an approximate side-view of the person. From a realistic viewpoint, such an assumption is impractical in surveillance applications and it is of interest to develop methods to synthesize a side view of the person, given an arbitrary view. For large distances from the camera, a planar approximation for the individual can be assumed. In this paper, we propose a perspective projection approach for recovering the direction of motion of the person purely from the video data, followed by synthesis of a new video sequence at a different angle. The algorithm works purely in the image and video domain, though 3D structure plays an implicit role in its theoretical justification. Examples of synthesized views using our method and performance evaluation are presented.},
	keywords = {(access, 3D, applications;, approach;, approximately, approximation;, arbitrary, Biometrics, control);, data;, direction, estimation;, evaluation;, Gait, image, monocular, MOTION, performance, perspective, planar, processing;, projection, recognition;, recovery;, scenes;, sequence;, sequences;, side, signal, structure;, Surveillance, surveillance;, synthesis;, synthesized, video, view, views;},
	doi = {10.1109/ICASSP.2003.1199520},
	author = {Chowdhury, A.K.R. and Kale, A. and Chellapa, Rama}
}
