Article Calibration of an Outdoor Distributed Camera Network with a 3D Point Cloud (original) (raw)

Calibration of an outdoor distributed camera network with a 3D point cloud

Sensors (Basel, Switzerland), 2014

Outdoor camera networks are becoming ubiquitous in critical urban areas of the largest cities around the world. Although current applications of camera networks are mostly tailored to video surveillance, recent research projects are exploiting their use to aid robotic systems in people-assisting tasks. Such systems require precise calibration of the internal and external parameters of the distributed camera network. Despite the fact that camera calibration has been an extensively studied topic, the development of practical methods for user-assisted calibration that minimize user intervention time and maximize precision still pose significant challenges. These camera systems have non-overlapping fields of view, are subject to environmental stress, and are likely to suffer frequent recalibration. In this paper, we propose the use of a 3D map covering the area to support the calibration process and develop an automated method that allows quick and precise calibration of a large camera ...

Calibrating an outdoor distributed camera network using Laser Range Finder data

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009

Outdoor camera networks are becoming ubiquitous in critical urban areas of large cities around the world. Although current applications of camera networks are mostly limited to video surveillance, recent research projects are exploiting advances on outdoor robotics technology to develop systems that put together networks of cameras and mobile robots in people assisting tasks. Such systems require the creation of robot navigation systems in urban areas with a precise calibration of the distributed camera network. Despite camera calibration has been an extensively studied topic, the calibration (intrinsic and extrinsic) of large outdoor camera networks with no overlapping view fields, and likely to suffer frequent recalibration, poses novel challenges in the development of practical methods for user-assisted calibration that minimize intervention times and maximize precision. In this paper we propose the utilization of Laser Range Finder (LRF) data covering the area of the camera network to support the calibration process and develop a semi-automated methodology allowing quick and precise calibration of large camera networks. The proposed methods have been tested in a real urban environment and have been applied to create direct mappings (homographies) between image coordinates and world points in the ground plane (walking areas) to support person and robot detection and localization algorithms.

Infrastructure-based calibration of a multi-camera rig

2014 IEEE International Conference on Robotics and Automation (ICRA), 2014

The online recalibration of multi-sensor systems is a fundamental problem that must be solved before complex automated systems are deployed in situations such as automated driving. In such situations, accurate knowledge of calibration parameters is critical for the safe operation of automated systems. However, most existing calibration methods for multisensor systems are computationally expensive, use installations of known fiducial patterns, and require expert supervision. We propose an alternative approach called infrastructure-based calibration that is efficient, requires no modification of the infrastructure, and is completely unsupervised. In a survey phase, a computationally expensive simultaneous localization and mapping (SLAM) method is used to build a highly accurate map of a calibration area. Once the map is built, many other vehicles are able to use it for calibration as if it were a known fiducial pattern.

Calibrating a Wide-Area Camera Network with Non-Overlapping Views Using Mobile Devices

In a wide-area camera network, cameras are often placed such that their views do not overlap. Collaborative tasks such as tracking and activity analysis still require discovering the network topology including the extrinsic calibration of the cameras. This work addresses the problem of calibrating a fixed camera in a wide-area camera network in a global coordinate system so that the results can be shared across calibrations. We achieve this by using commonly available mobile devices such as smartphones. At least one mobile device takes images that overlap with a fixed camera's view and records the GPS position and 3D orientation of the device when an image is captured. These sensor measurements (including the image, GPS position, and device orientation) are fused in order to calibrate the fixed camera. This article derives a novel maximum likelihood estimation formulation for finding the most probable location and orientation of a fixed camera. This formulation is solved in a distributed manner using a consensus algorithm. We evaluate the efficacy of the proposed methodology with several simulated and real-world datasets.

Extrinsic Calibration of Camera Networks Based on Pedestrians

Sensors, 2016

In this paper, we propose a novel extrinsic calibration method for camera networks by analyzing tracks of pedestrians. First of all, we extract the center lines of walking persons by detecting their heads and feet in the camera images. We propose an easy and accurate method to estimate the 3D positions of the head and feet w.r.t. a local camera coordinate system from these center lines. We also propose a RANSAC-based orthogonal Procrustes approach to compute relative extrinsic parameters connecting the coordinate systems of cameras in a pairwise fashion. Finally, we refine the extrinsic calibration matrices using a method that minimizes the reprojection error. While existing state-of-the-art calibration methods explore epipolar geometry and use image positions directly, the proposed method first computes 3D positions per camera and then fuses the data. This results in simpler computations and a more flexible and accurate calibration method. Another advantage of our method is that it can also handle the case of persons walking along straight lines, which cannot be handled by most of the existing state-of-the-art calibration methods since all head and feet positions are co-planar. This situation often happens in real life.

Multiple view camera calibration for localization

Distributed Smart Cameras, …, 2007

The recent development of distributed smart camera networks allows for automated multiple view processing. Quick and easy calibration of uncalibrated multiple camera setups is important for practical uses of such systems by non-experts and in temporary setups. In this paper we discuss options for calibration, illustrated with a basic two-camera setup where each camera is a smart camera mote with a highly parallel SIMD processor and an 8051 microcontroller. In order to accommodate arbitrary (lens) distortion, perspective mapping and transforms for which no analytic inverse is known, we propose the use of neural networks to map projective grid space back to Euclidean space for use in 3D localization and 3D view interpretation.

Calibrating Distributed Camera Networks

Proceedings of the IEEE, 2000

| Recent developments in wireless sensor networks have made feasible distributed camera networks, in which cameras and processing nodes may be spread over a wide geographical area, with no centralized processor and limited ability to communicate a large amount of information over long distances. This paper overviews distributed algorithms for the calibration of such camera networks-that is, the automatic estimation of each camera's position, orientation, and focal length. In particular, we discuss a decentralized method for obtaining the vision graph for a distributed camera network, in which each edge of the graph represents two cameras that image a sufficiently large part of the same environment. We next describe a distributed algorithm in which each camera performs a local, robust nonlinear optimization over the camera parameters and scene points of its vision graph neighbors in order to obtain an initial calibration estimate.

Using pedestrians walking on uneven terrains for camera calibration

Machine Vision and Applications, 2011

A calibrated camera is essential for computer vision systems: the prime reason being that such a camera acts as an angle measuring device. Once the camera is calibrated, applications like three-dimensional reconstruction or metrology or other applications requiring real world information from the video sequences can be envisioned. Motivated by this, we address the problem of calibrating multiple cameras, with