Aaron Mavrinac

Modeling Coverage in Camera Networks: A Survey

Modeling the coverage of a sensor network is an important step in a number of design and optimization techniques. The nature of vision sensors presents unique challenges in deriving such models for camera networks. A comprehensive survey of geometric and topological coverage models for camera networks from the literature is presented. The models are analyzed and compared in the context of their intended applications, and from this treatment the properties of a hypothetical inclusively general model of each type are derived.

A fuzzy model for coverage evaluation of cameras and multi-camera networks

A comprehensive, intuitive, task-oriented three-dimensional coverage model for cameras and multi-camera networks using fuzzy sets is presented. The model captures the vagueness inherent in the concept of visual coverage. At present, the model can be used to evaluate, given a scene model and an objective, the coverage performance of a given camera or multi-camera network configuration, as a single numerical metric. Plans to use the model for optimal camera placement and other problems involving coverage are discussed. Examples of qualitative experimental validation of the coverage model are presented.

Sensor planning for range cameras via a coverage strength model

A method for sensor planning based on a previously developed coverage strength model is presented. The approach taken is known as generate-and-test: a feasible solution is predefined and then tested using the coverage model. The relationship between the resolution of the imaging system and its performance is the key component to perform sensor planning of range cameras. Experimental results are presented; the inverse correlation between coverage performance and measurement error demonstrates the usefulness of the model in the sensor planning context.

Coverage quality and smoothness criteria for online view selection in a multi-camera network

The problem of online selection of monocular view sequences for an arbitrary task in a calibrated multi-camera network is investigated. An objective function for the quality of a view sequence is derived from a novel task-oriented, model-based instantaneous coverage quality criterion and a criterion of the smoothness of view transitions over time. The former is quantified by a priori information about the camera system, environment, and task generally available in the target application class. The latter is derived from qualitative definitions of undesirable transition effects. A scalable online algorithm with robust suboptimal performance is presented based on this objective function. Experimental results demonstrate the performance of the method—and therefore the criteria—as well as its robustness to several identified sources of nonsmoothness.

An automatic calibration method for stereo-based 3D distributed smart camera networks

Stereo-based 3D distributed smart camera networks are useful in a broad range of applications. Knowledge of the relative locations and orientations of nodes in the network is an essential prerequisite for true 3D sensing. A novel spatial calibration method for a network of pre-calibrated stereo smart cameras is presented, which obtains pose estimates suitable for collaborative 3D vision in a distributed fashion using two stages of registration on robust 3D point sets. The method is initially described in a geometrical sense, then presented in a practical implementation using existing vision and registration algorithms. Experiments using both software simulations and physical devices are designed and executed to demonstrate performance.

Calibration of dual laser-based range cameras for reduced occlusion in 3D imaging

A robust model-based calibration method for dual laser line active triangulation range cameras, with the goal of reducing camera occlusion via data fusion, is presented. The algorithm is split into two stages: line-based estimation of the lens distortion parameters in the individual cameras, and computation of the perspective transformation from each image to a common world frame in the laser plane using correspondences on a target with known geometry. Experimental results are presented, evaluating the accuracy of the calibration based on mean position error as well as the ability of the system to reduce camera occlusion.

Competitive Learning Techniques for Color Image Segmentation

A method for color image segmentation using a competitive learning clustering scheme is examined, and some basic improvements are made. Two important aspects of the color image segmentation problem, namely color space selection and oversegmentation, are discussed in the context of the algorithm, with comments about suitability and effectivenessof choices for various applications. A variety of settings are tested and compared to highlight performance.

Evaluating the fuzzy coverage model for 3D multi-camera network applications

An intuitive three-dimensional task-oriented coverage model for 3D multi-camera networks based on fuzzy sets is presented. The model captures the vagueness inherent in the concept of visual coverage, with a specific target of the feature detection and matching task. The coverage degree predicted by the model is validated against various multi-camera network configurations using the SIFT feature detection and description algorithm.

Task-oriented optimal view selection in a calibrated multi-camera system

A method for real-time selection of optimal view sequences for a task in a calibrated multi-camera system is presented. Selection is based on a multi-camera coverage model constructed from a priori information about the cameras, task, and scene, which are assumed to be available in the relatively controlled environments of the target application class. Experimental results demonstrate the generality and effectiveness of the algorithm and explore trade-offs in parameterization.

Optimizing load distribution in camera networks with a hypergraph model of coverage topology

A new topological model of camera network coverage, based on a weighted hypergraph representation, is introduced. The models theoretical basis is the coverage strength model, presented in previous work and summarized here. Optimal distribution of task processing is approximated by adapting a local search heuristic for parallel machine scheduling to this hypergraph model. Simulation results are presented to demonstrate its effectiveness.