Distributed Optimization of Visual Sensor Networks for Coverage of a Large-Scale 3-D Scene

Visual coverage is an important task for environment perception. In this article, the coverage of a large-scale 3-D scene represented by a polygon mesh model is considered, and a visual sensor network deployment algorithm is proposed through the combination of space partition, greedy and local search procedures. Comparing with existing approaches, the proposed algorithm can handle large-scale 3-D polygon meshes much faster in a scalable and distributed way, with superior coverage performance. First, we propose a new data structure called "chunk-triangle" in order to accelerate the computing process to identify visible triangles for a given camera. Furthermore, a GPU-based parallel algorithm is presented to shorten the time consumed for occlusion detection. Second, a new fast, scalable and distributed deployment approach is proposed for a camera sensor network to cover large-scale 3-D polygon meshes. The deployment algorithm generates a solution space of individual candidate cameras followed by camera selection. In camera selection, we partition the target scene space into some regions and conduct greedy search, respectively, in each region in order to choose a preliminary set of cameras with high initial coverage quality. Then, a local search strategy is further conducted to improve the coverage performance by compensating for the lost in rough space partition, and thus, results in an optimal deployment configuration of the camera network. Comparative evaluation results demonstrate the advantages of the proposed approach versus existing methods in terms of time cost, scalability, and coverage performance.