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A new topological model of camera network coverage, based on a weighted hypergraph representation, is introduced. The model's 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 t...
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... comparison, we also assigned the same event detections using four other orientations of H C : the optimal unweighted minimum maximum indegree orientation U , two random orientations R 1 and R 2 , and a greedy orientation G (edges oriented in arbitrary order to the vertex with least indegree). Figure 5 shows the maximum and standard deviation of processing loads (with a mean of 1378.39) for each strategy. ...
Citations
... Kulkarni et al. (2007) construct a vision graph using a Monte Carlo feature matching technique with a geometric model component, and demonstrate its use in duty cycling and triggered wake-up. Mavrinac and Chen (2011) propose a coverage hypergraph derived directly from their geometric coverage model, and apply it to the optimization of load distribution using a parallel machine scheduling algorithm. Table 2 compares the nature and properties of a selection of topological coverage overlap models from the literature, grouped by application (indicated in the first column). ...
... Kulkarni et al. (2007) model the full k-overlap topology of the camera network, although they do not explicitly formalize this model in a hypergraph representation or use any combinatorial techniques. Mavrinac and Chen (2011) present an explicit hypergraph representation of k-overlap topology, with an initial scheduling application using a combinatorial algorithm. ...
... Both describe methods of handling non-uniform spatial distributions of reference points. Mavrinac and Chen (2011) theoretically use the volume of intersection between k cameras' geometric coverage models to weight hyperedges, but in practice, the required polytope intersection procedure is NP-hard, so they use a uniform distribution of points to compute a discrete approximation. ...
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.
An automatic method for solving the problem of view planning in high-resolution industrial inspection is presented. The method's goal is to maximize the visual coverage, and to minimize the number of cameras used for inspection. Using a CAD model of the object of interest, we define the scene-points and the viewpoints, with the later being the solution space. The problem formulation accurately encapsulates all the vision-and task-related requirements of the design process for inspection systems. We use a graph-based approach to formulate a solution for the problem. The solution is implemented as a greedy algorithm, and the method is validated through experiments.
The employment of visual sensor networks for video surveillance has brought in as many challenges as advantages. While the integration of multiple cameras into a network has the potential advantage of fusing complementary observations from sensors and enlarging visual coverage, it also increases the complexity of tracking tasks and poses challenges to system scalability. For real time performance, a key approach to tackling these challenges is the mapping of the global tracking task onto a distributed sensing and processing infrastructure. In this paper, we present an efficient and scalable multi-camera multi-people tracking system with a three-layer architecture, in which we formulate the overall task (i.e., tracking all people using all available cameras) as a vision based state estimation problem and aim to maximize utility and sharing of available sensing and processing resources. By exploiting the geometric relations between sensing geometry and people’s positions, our method is able to dynamically and adaptively partition the overall task into a number of nearly independent subtasks with the aid of occlusion reasoning, each of which tracks a subset of people with a subset of cameras (or agencies). The method hereby reduces task complexity dramatically and helps to boost parallelization and maximize the system’s real time throughput and reliability while accounting for intrinsic uncertainty induced, e.g., by visual clutter and occlusions. We demonstrate the efficiency of our decentralized tracker on challenging indoor and outdoor video sequences.
A method for PTZ camera reconfiguration is presented. The objective of this work is to improve target tracking and surveillance applications in unmanned vehicles. Pan, tilt, and zoom configurations are computed transforming the visual constraints, given by a model of visual coverage, into geometric constraints. In the case of multiple targets the camera configurations are computed by a consensus algorithm. The approach is defined in a multi-agent framework allowing for scalability of the system, and cooperation between the cameras. Experimental results show the performance of the approach.
