Calibration of a reconfigurable array of omnidirectional cameras using a moving person

Reconfigurable arrays of omnidirectional cameras are useful for applications where multiple cameras working together are to be deployed at a short notice. This paper addresses the important issue of calibration of such arrays in terms of the relative camera positions and orientations. The lo-cation of a one-dimensional object moving parallel to itself, such as a moving person is used to establish correspondences between multiple cameras. In such case, the non-linear 3-D problem of calibration can be approximated by a 2-D problem in plan view. This enables an initial solution us-ing factorization method. A non-linear optimization stage is then used to account for the the approximations, as well as to minimize the geometric error between the observed and projected omni pixel coordinates. Experimental results with simulated and real data illustrate the effectiveness of the method. Categories and Subject Descriptor

Osteopontin Alleles Are Associated with Clinical Characteristics in Systemic Lupus Erythematosus

Variants of the osteopontin (OPN) gene have been associated with systemic lupus erythematosus (SLE) susceptibility and cytokine profiles in SLE patients. It is not known whether these alleles are associated with specific clinical phenotypes in SLE. We studied 252 well-characterized SLE patients from a multiethnic cohort, genotyping the rs11730582, rs28357094, rs6532040, and rs9138 SNPs in the OPN gene. Ancestry informative markers were used to control for genetic ancestry. The SLE-risk allele rs9138C in the 3′ UTR region was associated with photosensitivity in lupus patients across all ancestral backgrounds (meta-analysis OR = 3.2, 95% CI = 1.6–6.5, P = 1.0 × 10−3). Additionally, the promoter variant rs11730582C demonstrated suggestive evidence for association with two hematologic traits: thrombocytopenia (OR = 2.1, P = 0.023) and hemolytic anemia (OR = 2.6, P = 0.036). These clinical associations with SNPs in the promoter and 3′ UTR regions align with previously reported SLE-susceptibility SNPs in OPN and suggest potential roles for these variants in antibody-mediated cytopenias and skin inflammation in SLE

Parametric Ego-Motion Estimation for Vehicle Surround Analysis Using Omni-Directional Camera

Omni-directional cameras which give 360 degree panoramic view of the surroundings have recently been used in many applications such as robotics, navigation and surveillance. This paper describes the application of parametric ego-motion estimation for vehicle detection to perform surround analysis using an automobile mounted camera. For this purpose, the parametric planar motion model is integrated with the transformations to compensate distortion in omni-directional images. The framework is used to detect objects with independent motion or height above the road. Camera calibration as well as the approximate vehicle speed obtained from CAN bus are integrated with the motion information from spatial and temporal gradients using Bayesian approach. The approach is tested for various configurations of automobile mounted omni camera as well as rectilinear camera. Successful detection and tracking of moving vehicles, and generation of surround map is demonstrated for application to intelligent driver support. Key words Motion estimation, Panoramic vision, Intelligent vehicles, Driver support systems, Collision avoidance

Parametric ego-motion estimation for vehicle surround analysis using an omnidirectional camera

Omnidirectional cameras that give a 360degrees panoramic view of the surroundings have recently been used in many applications such as robotics, navigation, and surveillance. This paper describes the application of parametric ego-motion estimation for vehicle detection to perform surround analysis using an automobile-mounted camera. For this purpose, the parametric planar motion model is integrated with the transformations to compensate distortion in omnidirectional images. The framework is used to detect objects with independent motion or height above the road. Camera calibration as well as the approximate vehicle speed obtained from a CAN bus are integrated with the motion information from spatial and temporal gradients using a Bayesian approach. The approach is tested for various configurations of an automobile-mounted omni camera as well as a rectilinear camera. Successful detection and tracking of moving vehicles and generation of a surround map are demonstrated for application to intelligent driver support

Image based estimation of pedestrian orientation for improving path prediction

Pedestrian protection is an essential component of driver assistance systems. A pedestrian protection system should be able to predict the possibility of collision after detecting the pedestrian, and it is important to consider all the cues available in order to make that prediction. The direction in which the pedestrian is facing is one such cue that could be used in predicting where the pedestrian may move in future. This paper describes a novel approach to determine the pedestrianpsilas orientation using Support Vector Machine (SVM) based scheme. Instead of providing a hard decision, this scheme estimates the discrete probability distribution of the orientation. A Hidden Markov Model (HMM) is used to model the transitions between orientations over time and the orientation probabilities are integrated over time to get a more reliable estimate of orientation. Experiments showing the performance of estimating orientations are described to show the promise of the approach

Motion Analysis for Event Detection and Tracking with a Mobile Omni-Directional Camera

A mobile platform mounted with Omni-Directional Vision Sensor (ODVS) can be used to monitor large areas and detect interesting events such as independently moving persons and vehicles. To avoid false alarms due to extraneous features, the image motion induced by the moving platform should be compensated. This paper describes a formulation and application of parametric ego-motion compensation for an ODVS. Omni images give 360 degrees view of surroundings but undergo considerable image distortion. To account for these distortions, the parametric planar motion model is integrated with the transformations into omni image space. Prior knowledge of approximate camera calibration and camera speed are integrated with the estimation process using Bayesian approach. Iterative, coarse to fine, gradient based estimation is used to correct the motion parameters for vibrations and other inaccuracies in prior knowledge. Experiments with camera mounted on various types of mobile platforms demonstrate successful detection of moving persons and vehicles

Pedestrian Protection Systems: Issues, Survey, and Challenges

Awaiting permission to publish abstrac