@conference {13108,
	title = {Assigning cameras to subjects in video surveillance systems},
	booktitle = {Robotics and Automation, 2009. ICRA {\textquoteright}09. IEEE International Conference on},
	year = {2009},
	month = {2009/05//},
	pages = {837 - 843},
	abstract = {We consider the problem of tracking multiple agents moving amongst obstacles, using multiple cameras. Given an environment with obstacles, and many people moving through it, we construct a separate narrow field of view video for as many people as possible, by stitching together video segments from multiple cameras over time. We employ a novel approach to assign cameras to people as a function of time, with camera switches when needed. The problem is modeled as a bipartite graph and the solution corresponds to a maximum matching. As people move, the solution is efficiently updated by computing an augmenting path rather than by solving for a new matching. This reduces computation time by an order of magnitude. In addition, solving for the shortest augmenting path minimizes the number of camera switches at each update. When not all people can be covered by the available cameras, we cluster as many people as possible into small groups, then assign cameras to groups using a minimum cost matching algorithm. We test our method using numerous runs from different simulators.},
	keywords = {agent, algorithm;multiple, assignment;computation, augmenting, cameras;video, cost, detection;video, graph;camera, MATCHING, matching;minimum, matching;target, path;bipartite, reduction;maximum, segment;video, Surveillance, surveillance;, system;graph, theory;image, TIME, tracking;obstacle, tracking;video, video},
	doi = {10.1109/ROBOT.2009.5152753},
	author = {El-Alfy,H. and Jacobs, David W. and Davis, Larry S.}
}
@conference {12517,
	title = {Enforcing integrability by error correction using l1-minimization},
	booktitle = {Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on},
	year = {2009},
	month = {2009/06//},
	pages = {2350 - 2357},
	abstract = {Surface reconstruction from gradient fields is an important final step in several applications involving gradient manipulations and estimation. Typically, the resulting gradient field is non-integrable due to linear/non-linear gradient manipulations, or due to presence of noise/outliers in gradient estimation. In this paper, we analyze integrability as error correction, inspired from recent work in compressed sensing, particulary lscr<sub>0</sub> - lscr<sub>1</sub> equivalence. We propose to obtain the surface by finding the gradient field which best fits the corrupted gradient field in lscr<sub>1</sub> sense. We present an exhaustive analysis of the properties of lscr<sub>1</sub> solution for gradient field integration using linear algebra and graph analogy. We consider three cases: (a) noise, but no outliers (b) no-noise but outliers and (c) presence of both noise and outliers in the given gradient field. We show that lscr<sub>1</sub> solution performs as well as least squares in the absence of outliers. While previous lscr<sub>0</sub> - lscr<sub>1</sub> equivalence work has focused on the number of errors (outliers), we show that the location of errors is equally important for gradient field integration. We characterize the lscr<sub>1</sub> solution both in terms of location and number of outliers, and outline scenarios where lscr<sub>1</sub> solution is equivalent to lscr<sub>0</sub> solution. We also show that when lscr<sub>1</sub> solution is not able to remove outliers, the property of local error confinement holds: i.e., the errors do not propagate to the entire surface as in least squares. We compare with previous techniques and show that lscr<sub>1</sub> solution performs well across all scenarios without the need for any tunable parameter adjustments.},
	keywords = {-, algebra;lscr<sub>0</sub>, algebra;minimisation;, analogy;integrability;least, compressed, correction;gradient, equivalence;lscr<sub>1</sub>-minimization;noise-outlier;surface, estimation;gradient, field, integration;gradient, lscr<sub>1</sub>, manipulation;graph, methods;graph, reconstruction;error, reconstruction;linear, sensing;error, squares;linear, theory;image},
	doi = {10.1109/CVPR.2009.5206603},
	author = {Reddy, D. and Agrawal,A. and Chellapa, Rama}
}
@conference {13106,
	title = {Understanding videos, constructing plots learning a visually grounded storyline model from annotated videos},
	booktitle = {Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on},
	year = {2009},
	month = {2009/06//},
	pages = {2012 - 2019},
	abstract = {Analyzing videos of human activities involves not only recognizing actions (typically based on their appearances), but also determining the story/plot of the video. The storyline of a video describes causal relationships between actions. Beyond recognition of individual actions, discovering causal relationships helps to better understand the semantic meaning of the activities. We present an approach to learn a visually grounded storyline model of videos directly from weakly labeled data. The storyline model is represented as an AND-OR graph, a structure that can compactly encode storyline variation across videos. The edges in the AND-OR graph correspond to causal relationships which are represented in terms of spatio-temporal constraints. We formulate an Integer Programming framework for action recognition and storyline extraction using the storyline model and visual groundings learned from training data.},
	keywords = {(artificial, action, activity, analysis;integer, AND-OR, annotation;video, coding;, constraint;video, construction;semantic, extraction;graph, framework;plots, graph;encoding;human, grounded, intelligence);spatiotemporal, learning, meaning;spatio-temporal, model, phenomena;video, Programming, programming;learning, recognition;human, representation;integer, storyline, theory;image, understanding;visually},
	doi = {10.1109/CVPR.2009.5206492},
	author = {Gupta,A. and Srinivasan,P. and Shi,Jianbo and Davis, Larry S.}
}
@conference {13122,
	title = {Kernel integral images: A framework for fast non-uniform filtering},
	booktitle = {Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on},
	year = {2008},
	month = {2008/06//},
	pages = {1 - 8},
	abstract = {Integral images are commonly used in computer vision and computer graphics applications. Evaluation of box filters via integral images can be performed in constant time, regardless of the filter size. Although Heckbert (1986) extended the integral image approach for more complex filters, its usage has been very limited, in practice. In this paper, we present an extension to integral images that allows for application of a wide class of non-uniform filters. Our approach is superior to Heckbertpsilas in terms of precision requirements and suitability for parallelization. We explain the theoretical basis of the approach and instantiate two concrete examples: filtering with bilinear interpolation, and filtering with approximated Gaussian weighting. Our experiments show the significant speedups we achieve, and the higher accuracy of our approach compared to Heckbertpsilas.},
	keywords = {approximated, equations;interpolation;, filtering;bilinear, filtering;kernel, Gaussian, graphics;computer, images;approximation, integral, interpolation;computer, nonuniform, processing;integral, theory;filtering, theory;image, vision;fast, weighting},
	doi = {10.1109/CVPR.2008.4587641},
	author = {Hussein,M. and Porikli, F. and Davis, Larry S.}
}
@conference {12626,
	title = {Finding Gait in Space and Time},
	booktitle = {Pattern Recognition, 2006. ICPR 2006. 18th International Conference on},
	volume = {4},
	year = {2006},
	month = {2006///},
	pages = {586 - 589},
	abstract = {We describe an approach to characterize the signatures generated by walking humans in spatio-temporal domain. To describe the computational model for this periodic pattern, we take the mathematical theory of geometry group theory, which is widely used in crystallographic structure research. Both empirical and theoretical analyses prove that spatio-temporal helical patterns generated by legs belong to the Frieze Groups because they can be characterized by a repetitive motif along the direction of walking. The theory is applied to an automatic detection-and-tracking system capable of counting heads and handling occlusion by recognizing such patterns. Experimental results for videos acquired from both static and moving ground sensors are presented. Our algorithm demonstrates robustness to non-rigid human deformation as well as background clutter},
	keywords = {analysis;, analysis;geometry, analysis;group, characterization;occlusion, counting;mathematical, detection-and-tracking, domain;spatiotemporal, Frieze, group, groups;automatic, handling;pattern, helical, humans;computer, MOTION, pattern;spatiotemporal, patterns;walking, recognition;periodic, signature, system;computer, theory;head, theory;image, theory;motion, vision;gait},
	doi = {10.1109/ICPR.2006.562},
	author = {Yang Ran and Chellapa, Rama and Qinfen Zheng}
}
@conference {18226,
	title = {Image Tampering Identification using Blind Deconvolution},
	booktitle = {Image Processing, 2006 IEEE International Conference on},
	year = {2006},
	month = {2006/10//},
	pages = {2309 - 2312},
	abstract = {Digital images have been used in growing number of applications from law enforcement and surveillance, to medical diagnosis and consumer photography. With such widespread popularity and the presence of low-cost image editing softwares, the integrity of image content can no longer be taken for granted. In this paper, we propose a novel technique based on blind deconvolution to verify image authenticity. We consider the direct output images of a camera as authentic, and introduce algorithms to detect further processing such as tampering applied to the image. Our proposed method is based on the observation that many tampering operations can be approximated as a combination of linear and non-linear components. We model the linear part of the tampering process as a filter, and obtain its coefficients using blind deconvolution. These estimated coefficients are then used to identify possible manipulations. We demonstrate the effectiveness of the proposed image authentication technique and compare our results with existing works},
	keywords = {(access, approximation;blind, authentication;image, coding;, compression;deconvolution;filtering, control);data, deconvolution;camera;consumer, diagnosis;surveillance;tampering, editing, identification;approximation, images;filter, photography;digital, process;image, softwares;medical, theory;biometrics, theory;image},
	doi = {10.1109/ICIP.2006.312848},
	author = {Swaminathan,A. and M. Wu and Liu,K. J.R}
}
@conference {12625,
	title = {Segmentation and Probabilistic Registration of Articulated Body Models},
	booktitle = {Pattern Recognition, 2006. ICPR 2006. 18th International Conference on},
	volume = {2},
	year = {2006},
	month = {2006///},
	pages = {92 - 96},
	abstract = {There are different approaches to pose estimation and registration of different body parts using voxel data. We propose a general bottom-up approach in order to segment the voxels into different body parts. The voxels are first transformed into a high dimensional space which is the eigenspace of the Laplacian of the neighbourhood graph. We exploit the properties of this transformation and fit splines to the voxels belonging to different body segments in eigenspace. The boundary of the splines is determined by examination of the error in spline fitting. We then use a probabilistic approach to register the segmented body segments by utilizing their connectivity and prior knowledge of the general structure of the subjects. We present results on real data, containing both simple and complex poses. While we use human subjects in our experiment, the method is fairly general and can be applied to voxel-based registration of any articulated or non-rigid object composed of primarily 1-D parts},
	keywords = {(mathematics);, and, approach;eigenspace;image, articulated, body, data;voxel-based, eigenfunctions;graph, estimation;probabilistic, fitting;voxel, graph;pose, models;bottom-up, registration;eigenvalues, registration;image, registration;spline, segmentation;neighbourhood, segmentation;probability;splines, theory;image},
	doi = {10.1109/ICPR.2006.1034},
	author = {Sundaresan, A. and Chellapa, Rama}
}
