## 6 Conclusions and Future Work  
In this study, we first defined the assumptions and constraints of the camera placement problem for the surveillance of bridges. The objective of the defined problem is to minimize the total cost of cameras. To solve the problem within a reasonable amount of time, we proposed a greedy algorithm and GA with the visibility test to explore possible solutions effectively. We also developed a heuristic algorithm called ULA that incorporates the concepts of a uniqueness score and local search. Simulation results demonstrate that ULA outperforms the greedy algorithm and GA in terms of the average cost, coverage, and computation time for all problem sizes and minimum coverage levels. Further, we presented a case study of Samoonjin Bridge to illustrate the entire process of ULA.  
The main contribution of this study is the development of a heuristic algorithm to efficiently solve the camera placement problem. The increased complexity of the 3D space with the consideration of occlusions has triggered a necessity for the development of efficient algorithms to solve the problem. The proposed ULA's competitiveness with respect to efficiency and the ability to solve large camera placement problems was verified based on the results of the simulation that used real-world data. In this context, the ULA has numerous applications for various types of surveillance of important infrastructural components, including buildings and roads in addition to bridges. In addition, because of its ability to find a good solution quickly, the proposed algorithm can be used to consider changes in the environment. For example, various environments, such as budget levels and occluded points caused by dynamic obstacles, can be altered from time to time. Thus, a sensitivity analysis is helpful in considering these changes, and a quicker computation time is crucial in order to find a good solution in a variety of scenarios.  
Figure 9: Illustration of a camera placement using ULA. The blue spheres represent FoV of PTZ cameras.  
Nevertheless, the proposed approach has some limitations. First, because the proposed algorithm was based on a local search algorithm, it can converge to local optima. Second, the study did not consider mathematical approximation techniques for the camera placement problem.  
Future work could pursue several directions. First, the proposed algorithm can be applied to various objectives, including multi-objective optimization problems in which the objective function combines cost and coverage. In this case, by exploiting the proposed algorithm with a quick computation time, we can find Pareto frontiers for multi-objective optimization problems in various scenarios. Furthermore, in terms of dynamic objects, the use of drones with a camera may offer opportunities to expand the applications of the proposed algorithm. In addition, approaches for reducing the decision variables of mathematical models, the parameters, and the constraints and linear relaxations are all worthy of further investigation to verify the performance of the proposed algorithm more exhaustively. Finally, it is worthwhile to assess improving the ULA further to avoid converging to local optima.  
This research was supported by a grant (17 SCIP-B065985-05) from Smart Civil Infrastructure Research Program funded by Ministry of Land, Infrastructure and Transport (MOLIT) of Korea government and Korea Agency for Infrastructure Technology Advancement (KAIA).  
[PERSON] and [PERSON] conceived and designed the algorithms, especially [PERSON], who proposed the ULA; [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON], and [PERSON] wrote the paper.  
The authors declare no conflict of interest.