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

Detection of obstacles on runway using Ego-Motion compensation and tracking of significant features

This report describes a method for obstacle detection on a runway for autonomous navigation and landing of an aircraft. Detection is done in the presence of extraneous features such as tiremarks. Suitable features are extracted from the image and warping using approximately known camera and plane parameters is performed in order to compensate ego-motion as far as possible. Residual disparity after warping is estimated using an optical flow algorithm. Features are tracked from frame to frame so as to obtain more reliable estimates of their motion. Corrections are made to motion parameters with the residual disparities using a robust method, and features having large residual disparities are signaled as obstacles. Sensitivity analysis of the procedure is also studied. Nelson's optical flow constraint is proposed to separate moving obstacles from stationary ones. A Bayesian framework is used at every stage so that the confidence in the estimates can be determined

Algorithms for detection of objects in image sequences captured from an airborne imaging system

This research was initiated as a part of the effort at the NASA Ames Research Center to design a computer vision based system that can enhance the safety of navigation by aiding the pilots in detecting various obstacles on the runway during critical section of the flight such as a landing maneuver. The primary goal is the development of algorithms for detection of moving objects from a sequence of images obtained from an on-board video camera. Image regions corresponding to the independently moving objects are segmented from the background by applying constraint filtering on the optical flow computed from the initial few frames of the sequence. These detected regions are tracked over subsequent frames using a model based tracking algorithm. Position and velocity of the moving objects in the world coordinate is estimated using an extended Kalman filter. The algorithms are tested using the NASA line image sequence with six static trucks and a simulated moving truck and experimental results are described. Various limitations of the currently implemented version of the above algorithm are identified and possible solutions to build a practical working system are investigated

Physics Potential of the ICAL detector at the India-based Neutrino Observatory (INO)

The upcoming 50 kt magnetized iron calorimeter (ICAL) detector at the India-based Neutrino Observatory (INO) is designed to study the atmospheric neutrinos and antineutrinos separately over a wide range of energies and path lengths. The primary focus of this experiment is to explore the Earth matter effects by observing the energy and zenith angle dependence of the atmospheric neutrinos in the multi-GeV range. This study will be crucial to address some of the outstanding issues in neutrino oscillation physics, including the fundamental issue of neutrino mass hierarchy. In this document, we present the physics potential of the detector as obtained from realistic detector simulations. We describe the simulation framework, the neutrino interactions in the detector, and the expected response of the detector to particles traversing it. The ICAL detector can determine the energy and direction of the muons to a high precision, and in addition, its sensitivity to multi-GeV hadrons increases its physics reach substantially. Its charge identification capability, and hence its ability to distinguish neutrinos from antineutrinos, makes it an efficient detector for determining the neutrino mass hierarchy. In this report, we outline the analyses carried out for the determination of neutrino mass hierarchy and precision measurements of atmospheric neutrino mixing parameters at ICAL, and give the expected physics reach of the detector with 10 years of runtime. We also explore the potential of ICAL for probing new physics scenarios like CPT violation and the presence of magnetic monopoles.Comment: 139 pages, Physics White Paper of the ICAL (INO) Collaboration, Contents identical with the version published in Pramana - J. Physic

Motion analysis of omni-directional video streams for a mobile sentry

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 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 vehicle 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 a mobile platform demonstrate successful detection of moving persons and vehicles

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