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

Design and development of a ceiling-mounted workshop measurement positioning system for large-scale metrology

This paper presents a new ceiling-mounted workshop Measurement Positioning System (C-wMPS) compensating for many deficiencies shown by conventional metrology systems, especially on the possibility of task-oriented designing for coverage ability, measurement accuracy and efficiency. A hybrid calibration system consisting of a high-precision coordinate control field and standard lengths is developed and implemented for the C-wMPS, which can be designed concretely to provide both traceability and the ability of local accuracy enhancement. Layout optimization using a genetic algorithm based on grids is applied to design an appropriate layout of the system, therefore promotes the system’s performance and reduce cost. An experiment carried out at the Guidance, Navigation and Control laboratory (GNC lab, 40×30×12m) validates the prominent characteristic of C-wMPS and the fitness of the new calibration system and layout optimization method.<br/

Approximate Techniques in Solving Optimal Camera Placement Problems

While the theoretical foundation of the optimal camera placement problem has been studied for decades, its practical implementation has recently attracted significant research interest due to the increasing popularity of visual sensor networks. The most flexible formulation of finding the optimal camera placement is based on a binary integer programming (BIP) problem. Despite the flexibility, most of the resulting BIP problems are NP-hard and any such formulations of reasonable size are not amenable to exact solutions. There exists a myriad of approximate algorithms for BIP problems, but their applications, efficiency, and scalability in solving camera placement are poorly understood. Thus, we develop a comprehensive framework in comparing the merits of a wide variety of approximate algorithms in solving the optimal camera placement problems. We first present a general approach of adapting these problems into BIP formulations. Then, we demonstrate how they can be solved using different approximate algorithms including greedy heuristics, Markov-chain Monte Carlo, simulated annealing, and linear and semidefinite programming relaxations. 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