## 1 Introduction  
The increasing social demand for disaster-recovery systems has resulted in an increasing requirement for surveillance activities in core-infrastructure components. In the case of a natural disaster such as a flood or an earthquake, the infrastructure near the river--the bridges, in particular--plays an important role in the delivery of supplies to victims and in the repair of the damaged area in a timely manner. The placement of visual surveillance devices, such as a camera, can directly impact not only the total cost but also the coverage of surveillance targets. In this respect, the development of efficient algorithms for deploying cameras allows a detailed evaluation of damages sustained by a bridge within budget constraints. Algorithms also enable surveillance applications that require quick pre-emptive deployment when budget constraints and occluded points caused by static or dynamic obstacles change over time. In order to take into account more detailed scenarios, a quick computation time is crucial to find a new camera configuration in response to or to accommodate such changes. However, the development of an algorithm for the camera placement problem poses two challenges. The first challenge is the computational complexity of the problem. The combinatorial optimization problem of identifying the optimal placement of multiple cameras to observe the maximum space is NP-hard [1]. Particularly, in the case of placing cameras to monitor large infrastructures, the size of the problem and the number of decision variables increase as the number of feasible locations for the camera and the number of grid points increase due to the nature of the NP-hard problem. To address this issue, we present the visibility test with a minimum coverage level to efficiently reduce the search space. We present two well-known heuristic algorithms: a greedy algorithm and genetic algorithm. The greedy algorithm finds a solution from the myopic perspective and is commonly used in previous literature [2,3]. Genetic algorithms (GA) are widely used to solve the camera placement problem because these algorithms can probe the solution space and locate optima quickly with no prior information [4]. We also propose a new heuristic algorithm inspired by the uniqueness score, which puts a higher importance on a grid point of the target that is frequently left uncovered by other cameras. After finding an initial solution based on the sum of uniqueness scores, a local search phase improves it by changing the types of cameras chosen to reduce the total cost within the minimum coverage level. To validate the performance of the probabilistic search methods, we designed simulation experiments and compared the performance of the proposed algorithm with those of two well-known heuristic algorithms: a greedy algorithm and a genetic algorithm. The performance of the algorithm was measured by the average coverage from the experiments. The second challenge is related to the geographical features of the areas near bridges. In addition to the characteristics of the typical camera placement problem, the surveillance of bridges involves additional constraints related to the possible locations of the camera and occlusions. For example, because of the presence of rivers and the height of bridges, the areas where cameras can be placed are highly restricted to riverside areas or narrow areas on the bridges. Due to the rectangular shape of bridges, particularly the planes and edges, a part of the camera's view is obstructed and the underside of the bridge cannot be observed, despite the broader field of view of pan-tilt-zoom (PTZ) cameras. The occluded view of the camera, the restricted areas, and the broader field of view of the PTZ cameras result in an increased computation time and memory usage for the camera placement problem due to the higher number of possible configurations, overlapped areas, and occluded points.