Abstract

An optimal camera placement problem is investigated. The objective is to maximize the area of the field of view (FoV) of a stitched video obtained by stitching video streams from an array of cameras. The positions and poses of these cameras are restricted to a given set of selections. The camera array is designed to be placed inside the abdomen to support minimally invasive laparoscopic surgery. Hence, a few non-traditional requirements/constraints are imposed: Adjacent views are required to overlap to support image registration for seamless video stitching. The resulting effective FoV should be a contiguous region without any holes and should be a convex polygon. With these requirements, traditional camera placement algorithms cannot be directly applied to solve this problem. In this work, we show the complexity of this problem grows exponentially as a function of the problem size, and then present a greedy polynomial time heuristic solution that approximates well to the globally optimal solution. We present a new approach to directly evaluate the combined coverage area (area of FoV) as the union of a set of quadrilaterals. We also propose a graph-based approach to ensure the stitching requirement (overlap between adjacent views) is satisfied. We present a method to find a convex polygon with maximum area from a given polygon. Several design examples show that the proposed algorithm can achieve larger FoV area while using much less computing time.

Highlights

  • Camera arrays have extensive applications in surveillance [1], robotics [2], Virtual Reality [3,4,5], surgery [6], and more

  • While the maximal area approach unsurprisingly achieved a larger area than the maximal convex region approach and the resulting coverage region is nicely symmetrical, as we can see in Figure 5, removing the region of interest can cause the construction of the array to become very irregular and it would not be very useful for most mosaicking applications

  • The maximal convex region approach is able to generate a region which is much more similar to what could be seen by a single large sensor as we desire for image stitching

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Summary

Introduction

Camera arrays have extensive applications in surveillance [1], robotics [2], Virtual Reality [3,4,5], surgery [6], and more. The traditional camera placement problem has been investigated in the context of video surveillance [7,8,9] where cameras are to be placed in a three-dimensional space to cover a two-dimensional plane [10]. Optimal camera placement under these restrictions has been previously investigated [11,12]. There are two types of camera placement problem formulations: MIN and FIX [9]. The goal of MIN formulation is to minimize the number of cameras needed to cover a given area. The goal of the Sensors 2018, 18, 2284; doi:10.3390/s18072284 www.mdpi.com/journal/sensors

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