622 research outputs found
Hybrid Focal Stereo Networks for Pattern Analysis in Homogeneous Scenes
In this paper we address the problem of multiple camera calibration in the
presence of a homogeneous scene, and without the possibility of employing
calibration object based methods. The proposed solution exploits salient
features present in a larger field of view, but instead of employing active
vision we replace the cameras with stereo rigs featuring a long focal analysis
camera, as well as a short focal registration camera. Thus, we are able to
propose an accurate solution which does not require intrinsic variation models
as in the case of zooming cameras. Moreover, the availability of the two views
simultaneously in each rig allows for pose re-estimation between rigs as often
as necessary. The algorithm has been successfully validated in an indoor
setting, as well as on a difficult scene featuring a highly dense pilgrim crowd
in Makkah.Comment: 13 pages, 6 figures, submitted to Machine Vision and Application
Calibration with concurrent PT axes
The introduction of active (pan-tilt-zoom or PTZ) cameras in Smart Rooms in addition to fixed static cameras
allows to improve resolution in volumetric reconstruction, adding the capability to track smaller objects with
higher precision in actual 3D world coordinates. To accomplish this goal, precise camera calibration data
should be available for any pan, tilt, and zoom settings of each PTZ camera. The PTZ calibration method proposed
in this paper introduces a novel solution to the problem of computing extrinsic and intrinsic parameters
for active cameras. We first determine the rotation center of the camera expressed under an arbitrary world
coordinate origin. Then, we obtain an equation relating any rotation of the camera with the movement of the
principal point to define extrinsic parameters for any value of pan and tilt. Once this position is determined,
we compute how intrinsic parameters change as a function of zoom. We validate our method by evaluating the
re-projection error and its stability for points inside and outside the calibration set.Postprint (published version
Development of an Active Vision System for the Remote Identification of Multiple Targets
This thesis introduces a centralized active vision system for the remote identification of multiple targets in applications where the targets may outnumber the active system resources. Design and implementation details of a modular active vision system are presented, from which a prototype has been constructed. The system employs two different, yet complimentary, camera technologies. Omnidirectional cameras are used to detect and track targets at a low resolution, while perspective cameras mounted to pan-tilt stages are used to acquire high resolution images suitable for identification. Five greedy-based scheduling policies have been developed and implemented to manage the active system resources in an attempt to achieve optimal target-to-camera assignments. System performance has been evaluated using both simulated and real-world experiments under different target and system configurations for all five scheduling policies. Parameters affecting performance that were considered include: target entry conditions, congestion levels, target to camera speeds, target trajectories, and number of active cameras. An overall trend in the relative performance of the scheduling algorithms was observed. The Least System Reconfiguration and Future Least System Reconfiguration scheduling policies performed the best for the majority of conditions investigated, while the Load Sharing and First Come First Serve policies performed the poorest. The performance of the Earliest Deadline First policy was seen to be highly dependent on target predictability
Development of an Active Vision System for the Remote Identification of Multiple Targets
This thesis introduces a centralized active vision system for the remote identification of multiple targets in applications where the targets may outnumber the active system resources. Design and implementation details of a modular active vision system are presented, from which a prototype has been constructed. The system employs two different, yet complimentary, camera technologies. Omnidirectional cameras are used to detect and track targets at a low resolution, while perspective cameras mounted to pan-tilt stages are used to acquire high resolution images suitable for identification. Five greedy-based scheduling policies have been developed and implemented to manage the active system resources in an attempt to achieve optimal target-to-camera assignments. System performance has been evaluated using both simulated and real-world experiments under different target and system configurations for all five scheduling policies. Parameters affecting performance that were considered include: target entry conditions, congestion levels, target to camera speeds, target trajectories, and number of active cameras. An overall trend in the relative performance of the scheduling algorithms was observed. The Least System Reconfiguration and Future Least System Reconfiguration scheduling policies performed the best for the majority of conditions investigated, while the Load Sharing and First Come First Serve policies performed the poorest. The performance of the Earliest Deadline First policy was seen to be highly dependent on target predictability
Towards A Self-calibrating Video Camera Network For Content Analysis And Forensics
Due to growing security concerns, video surveillance and monitoring has received an immense attention from both federal agencies and private firms. The main concern is that a single camera, even if allowed to rotate or translate, is not sufficient to cover a large area for video surveillance. A more general solution with wide range of applications is to allow the deployed cameras to have a non-overlapping field of view (FoV) and to, if possible, allow these cameras to move freely in 3D space. This thesis addresses the issue of how cameras in such a network can be calibrated and how the network as a whole can be calibrated, such that each camera as a unit in the network is aware of its orientation with respect to all the other cameras in the network. Different types of cameras might be present in a multiple camera network and novel techniques are presented for efficient calibration of these cameras. Specifically: (i) For a stationary camera, we derive new constraints on the Image of the Absolute Conic (IAC). These new constraints are shown to be intrinsic to IAC; (ii) For a scene where object shadows are cast on a ground plane, we track the shadows on the ground plane cast by at least two unknown stationary points, and utilize the tracked shadow positions to compute the horizon line and hence compute the camera intrinsic and extrinsic parameters; (iii) A novel solution to a scenario where a camera is observing pedestrians is presented. The uniqueness of formulation lies in recognizing two harmonic homologies present in the geometry obtained by observing pedestrians; (iv) For a freely moving camera, a novel practical method is proposed for its self-calibration which even allows it to change its internal parameters by zooming; and (v) due to the increased application of the pan-tilt-zoom (PTZ) cameras, a technique is presented that uses only two images to estimate five camera parameters. For an automatically configurable multi-camera network, having non-overlapping field of view and possibly containing moving cameras, a practical framework is proposed that determines the geometry of such a dynamic camera network. It is shown that only one automatically computed vanishing point and a line lying on any plane orthogonal to the vertical direction is sufficient to infer the geometry of a dynamic network. Our method generalizes previous work which considers restricted camera motions. Using minimal assumptions, we are able to successfully demonstrate promising results on synthetic as well as on real data. Applications to path modeling, GPS coordinate estimation, and configuring mixed-reality environment are explored
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