On the Camera Placement Problem

As markers for visual sensor networks have become larger, interest in the optimal camera placement problem has continued to increase. The most featured solution for the optimal camera placement problem is based on binary integer programming (BIP). Due to the NP-hard characteristic of the optimal camera placement problem, however, it is difficult to find a solution for a complex, real-world problem using BIP. Many approximation algorithms have been developed to solve this problem. In this paper, a two-phase algorithm is proposed as an approximation algorithm based on BIP that can solve the optimal camera placement problem for a placement space larger than in current studies. This study solves the problem in three-dimensional space for a real-world structure.

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Optimal Camera Pose and Placement Configuration for Maximum Field-of-View Video Stitching

Sensors ◽

10.3390/s18072284 ◽

2018 ◽

Vol 18 (7) ◽

pp. 2284 ◽

Cited By ~ 2

Author(s):

Alex Watras ◽

Jae-Jun Kim ◽

Hewei Liu ◽

Yu Hu ◽

Hongrui Jiang

Keyword(s):

Convex Polygon ◽

Computing Time ◽

Optimal Solution ◽

Field Of View ◽

Problem Size ◽

Placement Problem ◽

Camera Placement ◽

Coverage Area ◽

Heuristic Solution ◽

Video Stitching

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.

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Hybrid differential evolution algorithms for the optimal camera placement problem

Journal of Systems and Information Technology ◽

10.1108/jsit-09-2017-0081 ◽

2018 ◽

Vol 20 (4) ◽

pp. 446-467 ◽

Cited By ~ 2

Author(s):

Mathieu Brévilliers ◽

Julien Lepagnot ◽

Lhassane Idoumghar ◽

Maher Rebai ◽

Julien Kritter

Keyword(s):

Differential Evolution ◽

Optimization Problems ◽

Set Covering ◽

Covering Problem ◽

Placement Problem ◽

Camera Placement ◽

Content Type ◽

Combinatorial Optimization Problems ◽

Evolution Algorithms ◽

Problem Instances

PurposeThis paper aims to investigate to what extent hybrid differential evolution (DE) algorithms can be successful in solving the optimal camera placement problem.Design/methodology/approachThis problem is stated as a unicost set covering problem (USCP) and 18 problem instances are defined according to practical operational needs. Three methods are selected from the literature to solve these instances: a CPLEX solver, greedy algorithm and row weighting local search (RWLS). Then, it is proposed to hybridize these algorithms with two hybrid DE approaches designed for combinatorial optimization problems. The first one is a set-based approach (DEset) from the literature. The second one is a new similarity-based approach (DEsim) that takes advantage of the geometric characteristics of a camera to find better solutions.FindingsThe experimental study highlights that RWLS and DEsim-CPLEX are the best proposed algorithms. Both easily outperform CPLEX, and it turns out that RWLS performs better on one class of problem instances, whereas DEsim-CPLEX performs better on another class, depending on the minimal resolution needed in practice.Originality/valueUp to now, the efficiency of RWLS and the DEset approach has been investigated only for a few problems. Thus, the first contribution is to apply these methods for the first time in the context of camera placement. Moreover, new hybrid DE algorithms are proposed to solve the optimal camera placement problem when stated as a USCP. The second main contribution is the design of the DEsim approach that uses the distance between camera locations to fully benefit from the DE mutation scheme.

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