Automated Placement of Cameras in a Floorplan to Satisfy Task-Specific Constraints (original) (raw)

Optimal Placement of Cameras in Floorplans to Satisfy Task Requirements and Cost Constraints

2004

In many multi-camera vision systems the effect of camera locations on the task-specific quality of service is ignored. Researchers in Computational Geometry have proposed elegant solutions for some sensor location problem classes. Unfortunately, these solutions utilize unrealistic assumptions about the cameras' capabilities that make these algorithms unsuitable for many real-world computer vision applications. In this paper, the general camera placement problem is first defined with assumptions that are more consistent with the capabilities of realworld cameras. Given a floorplan to be observed, the problem is to efficiently compute a camera layout such that certain task-specific constraints are met and with minimal camera setup cost. A solution to this problem is obtained via binary optimization over a discrete problem space. In preliminary experiments the performance of the system is demonstrated with two different practical experiments on a real floorplan.

Automated camera layout to satisfy task-specific and floor plan-specific coverage requirements

Computer Vision and Image Understanding, 2006

In many multi-camera vision systems the effect of camera locations on the task-specific quality of service is ignored. Researchers in Computational Geometry have proposed elegant solutions for some sensor location problem classes. Unfortunately, these solutions use unrealistic assumptions about the cameras' capabilities that make these algorithms unsuitable for many real world computer vision applications. In this paper, the general camera placement problem is first defined with assumptions that are more consistent with the capabilities of real world cameras. The region to be observed by cameras may be volumetric, static or dynamic, and may include holes. A subclass of this general problem can be formulated in terms of planar regions that are typical of building floor plans. Given a floor plan to be observed, the problem is then to reliably compute a camera layout such that certain task-specific constraints are met. A solution to this problem is obtained via binary optimization over a discrete problem space. In experiments the performance of the resulting system is demonstrated with different real indoor and outdoor floor plans.

Optimization of the location of camera in two dimensional floor layout

2013

Installation of the security cameras is increasing rapidly in our society that required a secure environment. It motivates us to discover an optimum camera placement in order to improve the coverage of a camera network. It is a significant design problem in order to have a proper camera placement in a distributed smart camera network by considering the number of cameras required. Thus, a method was proposed in order to determine the camera placement by using C and FORTRAN language. Besides that, it is advantageous to maximize the coverage area by using a minimum number of cameras. Hence, in order to reduce the number of cameras used, we divide the area of polygon into grid points. Then, we calculate the camera locations which can cover the grid points as much as possible. We formulate the above problem as a set of maximizing coverage problem. Moreover, the optimal camera problem was solved by developing a general visibility model for visual camera networks through Binary Integer Pro...

Camera Placement Meeting Restrictions of Computer Vision

2020 IEEE International Conference on Image Processing (ICIP), 2020

In the blooming era of smart edge devices, surveillance cameras have been deployed in many locations. Surveillance cameras are most useful when they are spaced out to maximize coverage of an area. However, deciding where to place cameras is an NP-hard problem and researchers have proposed heuristic solutions. Existing work does not consider a significant restriction of computer vision: in order to track a moving object, the object must occupy enough pixels. The number of pixels depends on many factors (How far away is the object? What is the camera resolution? What is the focal length?). In this study, we propose a camera placement method that identifies effective camera placement in arbitrary spaces and can account for different camera types as well. Our strategy represents spaces as polygons, then uses a greedy algorithm to partition the polygons and determine the cameras' locations to provide the desired coverage. Our solution also makes it possible to perform object tracking via overlapping camera placement. Our method is evaluated against complex shapes and real-world museum floor plans, achieving up to 85% coverage and 25% overlap.

Automatic camera placement for robot vision tasks

1995

Remote sensors such as CCD cameras can be used for a variety of robot sensing tasks, but given restrictions on camera location and imaging geometry, task constraints, and visual occlusion it can be difficult to find viewing positions from which the task can be completed. The complexity of these constraints suggests that automated, quantitative methods of sensor placement are likely to be useful, particularly when the workspace is cluttered and a mobile robot-mounted sensor is being used to increase the sensible region, circumvent occlusions, and so forth.

Optimal Camera Placement for 3D Environment

Communications in Computer and Information Science, 2011

Efficient camera placement is important in order to make sure the cost of a monitoring system is not higher than what it should be. This is also to ensure the maintenance of that system will not be complex and take longer time. Based on these issues, it has become an important requirement to optimize the number of the camera in camera placement system inside particular environment. This problem is based on the well-known Art Gallery Problem but most of previous works only proposed solution to this problem in 2D. We propose a method for finding the minimum number of cameras that can observe maximum space of 3D environment. In this method we assume that each of the cameras has limited field of view of 90o and only to be placed on the wall of the environment. Placement in 3D environment uses volume approach that takes frustum's volume and space's volume to calculate minimum number of camera.

Designing Camera Networks by Convex Quadratic Programming

Computer Graphics Forum, 2015

Figure 1: Example result of our proposed optimal camera placement framework. In a particular scenario, the user inputs a 3D floorplan that can be generated by processing an overhead 2D floorplan using the user-friendly GUI we developed. After setting certain camera parameters (e.g. field-of-view and depth-of-field), our approach computes a placement solution that can either maximize 3D floorplan coverage with a limited number of cameras or minimize the number of cameras needed to cover the entire floorplan. Unlike other placement methods, our approach is computationally efficient because it solves a constrained convex quadratic program. It also allows pairwise camera interactions to be directly encoded, which is quite useful for multiview applications, such as 3D reconstruction and surveillance.

Approximate techniques in solving optimal camera placement problems

2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 2011

While the theoretical foundation of optimal camera placement has been studied for decades, its practical implementation has recently attracted significant research interest due to the increasing popularity of visual sensor network. The discrete camera placement problem is NP-hard and many approximate solutions have been independently studied. The goal of this paper is to provide a comprehensive framework in comparing the merits of these techniques. We consider two general classes of camera placement problems and adapt some of the most commonly used approximation techniques in solving them. The accuracy, efficiency and scalability of each technique are analyzed and compared in depth. Extensive experimental results are provided to illustrate the strength and weakness of each method. Submission #662. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.

Camera Placement Optimization Conditioned on Human Behavior and 3D Geometry

2016

This paper proposes an algorithm to optimize the placement of surveillance cameras in a 3D infrastructure. The key differentiating feature in the algorithm design is the incorporation of human behavior within the infrastructure for optimization. Infrastructures depending on their geometries may exhibit regions with dominant human activity. In the absence of observations, this paper presents a method to predict this human behavior and identify such regions to deploy an effective surveillance scenario. Domain knowledge regarding the infrastructure was used to predict the possible human motion trajectories in the infrastructure. These trajectories were used to identify areas with dominant human activity. Furthermore, a metric that quantifies the position and orientation of a camera based on the observable space, activity in the space, pose of objects of interest within the activity, and their image resolution in camera view was defined for optimization. This method was compared with th...

Optimizing Camera Placements for Overlapped Coverage with 3D Camera Projections

2022 International Conference on Robotics and Automation (ICRA)

This paper proposes a method to compute camera 6 DoF poses to achieve a user defined coverage. The camera placement problem is modeled as a combinatorial optimization where given the maximum number of cameras, a camera set is selected from a larger pool of possible camera poses. We propose to minimize the squared error between the desired and the achieved coverage, and formulate the non-linear cost function as a mixed integer linear programming problem. A camera lens model is utilized to project the camera's view on a 3D voxel map to compute a coverage score which makes the optimization problem in real environments tractable. Experimental results in two real retail store environments demonstrate the better performance of the proposed formulation in terms of coverage and overlap for triangulation compared to existing methods.