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This paper investigates coverage control for visual sensor networks based ongradient descent techniques on matrix manifolds. We consider the scenario thatnetworked vision sensors with controllable orientations are distributed over3-D space to monitor 2-D environment. Then, the decision variable must beconstrained on the Lie group SO(3). The contribution of this paper is twofolds. The first one is technical, namely we formulate the coverage problem asan optimization problem on SO(3) without introducing local parameterizationlike Eular angles and directly apply the gradient descent algorithm on themanifold. The second technological contribution is to present not only thecoverage control scheme but also the density estimation process including imageprocessing and curve fitting while exemplifying its effectiveness throughsimulation of moving objects monitoring.

3-D Visual Coverage Based on Gradient Descent Techniques on Matrix Manifold and Its Application to Moving Objects Monitoring