Placement, visibility and coverage analysis of dynamic pan/tilt/zoom camera sensor networks

Multi-camera vision systems have important application in a number of fields, including robotics and security. One interesting problem related to multi-camera vision systems is to determine the effect of camera placement on the quality of service provided by a network of Pan/Tilt/Zoom (PTZ) cameras with respect to a specific image processing application. The goal of this work is to investigate how to place a team of PTZ cameras, potentially used for collaborative tasks, such as surveillance, and analyze the dynamic coverage that can be provided by them. Computational Geometry approaches to various formulations of sensor placement problems have been shown to offer very elegant solutions; however, they often involve unrealistic assumptions about real-world sensors, such as infinite sensing range and infinite rotational speed. Other solutions to camera placement have attempted to account for the constraints of real-world computer vision applications, but offer solutions that are approximations over a discrete problem space. A contribution of this work is an algorithm for camera placement that leverages Computational Geometry principles over a continuous problem space utilizing a model for dynamic camera coverage that is simple, yet representative. This offers a balance between accounting for real-world application constraints and creating a problem that is tractable

Camera placement optimization in object localization system

This paper focuses on the placement of cameras in order to achieve the highest possible localization accuracy with a multi-camera system. The cameras have redundant fields of view. They have to be placed according to some natural constraints but user defined constraints are allowed as well. A camera model is described and the components causing the localization errors are identified. Some localization accuracy measures are defined for any number of cameras. The multi-camera placement is analytically formulated using the expanded measures for multiple cameras. An example of placing two cameras is shown and the generalizations into higher dimensional parameter spaces are examined. There are publications where camera placement algorithms are formulated or compared. We make an attempt to examine the analytical solution of this problem in case of different objective functions

Camera Placement Meeting Restrictions of Computer Vision

In the blooming era of smart edge devices, surveillance cam- eras have been deployed in many locations. Surveillance cam- eras are most useful when they are spaced out to maximize coverage of an area. However, deciding where to place cam- eras is an NP-hard problem and researchers have proposed heuristic solutions. Existing work does not consider a signifi- cant 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 not only identifies effective camera placement in arbitrary spaces, but can account for different camera types as well. Our strat- egy represents spaces as polygons, then uses a greedy algo- rithm to partition the polygons and determine the cameras’ lo- cations to provide desired coverage. The 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 82% cov- erage and 28% overlap

Optimizing Fiducial Marker Placement for Improved Visual Localization

Adding fiducial markers to a scene is a well-known strategy for making visual localization algorithms more robust. Traditionally, these marker locations are selected by humans who are familiar with visual localization techniques. This paper explores the problem of automatic marker placement within a scene. Specifically, given a predetermined set of markers and a scene model, we compute optimized marker positions within the scene that can improve accuracy in visual localization. Our main contribution is a novel framework for modeling camera localizability that incorporates both natural scene features and artificial fiducial markers added to the scene. We present optimized marker placement (OMP), a greedy algorithm that is based on the camera localizability framework. We have also designed a simulation framework for testing marker placement algorithms on 3D models and images generated from synthetic scenes. We have evaluated OMP within this testbed and demonstrate an improvement in the localization rate by up to 20 percent on three different scenes

Camera and light placement for automated assembly inspection

Includes bibliographical references.Visual assembly inspection can provide a low cost, accurate, and efficient solution to the automated assembly inspection problem, which is a crucial component of any automated assembly manufacturing process. The performance of such an inspection system is heavily dependent on the placement of the camera and light source. This article presents new algorithms that use the CAD model of a finished assembly for placing the camera and light source to optimize the performance of an automated assembly inspection algorithm. This general-purpose algorithm utilizes the component material properties and the contact information from the CAD model of the assembly, along with standard computer graphics hardware and physically accurate lighting models, to determine the effects of camera and light source placement on the performance of an inspection algorithm. The effectiveness of the algorithms is illustrated on a typical mechanical assembly.This work was supported by National Science Foundation grant number CDR 8803017 to the Engineering Research Center for Intelligent Manufacturing Systems, National Science Foundation grant number MIP93-00560, an AT&T Bell Laboratories PhD Scholarship, and the NEC Corporation

A generic model for camera based intelligent road crowd control

Traffic flow control is often a big problem in many big cities in the world, especially during the peak and off-peak hours. Researchers are trying to find the optimal solution to solve this daily problem. Often, the problem is caused by the poor traffic signal light control system. Improper placement of the signal light and timing is the main issue. The problem can be solved by proper time management for the traffic signal through the congested and often over crowded areas. This research proposes a model for intelligent traffic flow control by implementing camera based surveillance and feedback system. A series of cameras are set minimum three signals ahead from the target junction. The complete software system is developed to help integrating the multiple camera on road as feedback to the signal light control systems.Keywords: surveillance; traffic flow; network; vehicles

Shape from inconsistent silhouette: Reconstruction of objects in the presence of segmentation and camera calibration error

Silhouettes are useful features to reconstruct the object shape when the object is textureless or the shape classes of objects are unknown. In this dissertation, we explore the problem of reconstructing the shape of challenging objects from silhouettes under real-world conditions such as the presence of silhouette and camera calibration error. This problem is called the Shape from Inconsistent Silhouettes problem. A psuedo-Boolean cost function is formalized for this problem, which penalizes differences between the reconstruction images and the silhouette images, and the Shape from Inconsistent Silhouette problem is cast as a psuedo-Boolean minimization problem. We propose a memory and time efficient method to find a local minimum solution to the optimization problem, including heuristics that take into account the geometric nature of the problem. Our methods are demonstrated on a variety of challenging objects including humans and large, thin objects. We also compare our methods to the state-of-the-art by generating reconstructions of synthetic objects with induced error. ^ We also propose a method for correcting camera calibration error given silhouettes with segmentation error. Unlike other existing methods, our method allows camera calibration error to be corrected without camera placement constraints and allows for silhouette segmentation error. This is accomplished by a modified Iterative Closest Point algorithm which minimizes the difference between an initial reconstruction and the input silhouettes. We characterize the degree of error that can be corrected with synthetic datasets with increasing error, and demonstrate the ability of the camera calibration correction method in improving the reconstruction quality in several challenging real-world datasets

Hybrid differential evolution algorithms for the optimal camera placement problem

Purpose – This paper investigates to what extent hybrid differential evolution (DE) algorithms can be successful in solving the optimal camera placement problem. Design/methodology/approach – This problem is stated as a unicost set covering problem (USCP) and 18 problem instances are defined according to practical operational needs. Three methods are selected from the literature to solve these instances: a CPLEX solver, a greedy algorithm, and a row weighting local search (RWLS). Then, it is proposed to hybridize these algorithms with two DE approaches designed for combinatorial optimization problems. The first one is a set-based approach (DEset) from the literature. The second one is a new similarity-based approach (DEsim) that takes advantage of the geometric characteristics of a camera in order to find better solutions. Findings – The experimental study highlights that RWLS and DEsim-CPLEX are the best proposed algorithms. Both easily outperform CPLEX, and it turns out that RWLS performs better on one class of problem instances, whereas DEsim-CPLEX performs better on another class, depending on the minimal resolution needed in practice. Originality/value – Up to now, the efficiency of RWLS and the DEset approach has been investigated only for a few problems. Thus, the first contribution is to apply these methods for the first time in the context of camera placement. Moreover, new hybrid DE algorithms are proposed to solve the optimal camera placement problem when stated as a USCP. The second main contribution is the design of the DEsim approach that uses the distance between camera locations in order to fully benefit from the DE mutation scheme

Optimal placement of security camera

This research studies the optimization on the placement of security camera. We need to improve the field of view (FOV) coverage of a security camera by only adjusting the location of cameras with minimal numbers of cameras. Two dimensional of floor plan was designed in boundary nodes and internal nodes. Cameras were installed at boundary node in order to view the internal nodes. We propose Binary Integer Programming method which can efficiently find an optimal layout for each camera. Heuristic method of Particle Swarm Optimization (PSO) algorithm is applied on this problem. As a result of this optimization, the FOV coverage of the whole camera network is maximized. This study show that the proposed PSO method perform well and effectively applied in placement of security camera on any design of floor plan

Sport Accessory Design for Narrative Clip 2

This report describes the development of mounts for the company Narrative’s camera Narrative Clip 2, focusing on people with an active lifestyle. The terminal goal of the thesis is to come down to which types of mounts to develop for the active lifestyle user and thereafter, create a couple of 3D-printed mounts for these placements. Moreover, suggestions for optimizing the Clip itself and its user-interaction for the purpose are desirable in the thesis. The first step of the implementation was to understand the needs of potential customers and get insight in where a placement of the Narrative Clip 2 would be the most suitable for their activity/sport. Hence, surveys were sent out to Swedish and international potential customers. The placements compiled are bicycle handlebars and helmets and the following needs were compiled for the mounts: secure, small size, holds the camera steady and possibility to rotate the camera. Also, the following desires were compiled: pliable and attachable to all handlebars/helmets. In the second step of the implementation, the concept generation phase, the problem was divided into subproblems making it easier to focus on one problem at a time. The subproblems were solved respectively by internal and external search, whereas various concept ideas were generated and sketched. Subsequently, the concept ideas used in the final prototype were selected by discussions, concept screening, testing of simple prototypes and a workshop. The result of the thesis are mounts including a ball-joint for rotation, a snap-fit case where to attach the camera and bottom plates that can be varied depending on placement; one for helmets and one for handlebars. The ball-joint and the snap-fit case are assembled with screws, making it possible to adjust the angle of the camera even more than with only the ball-joint. The ball-joint and the snap-fit case can be moved between the two bottom plates as it attaches with magnets - a feature making it easy to vary if you want to switch location of your camera between your helmet and your handlebar