Precise optimization of the surveillance camera placement on dynamic construction sites

It is known that the construction industry is one of the most dangerous sectors, with an exceptionally high proportion of professional injuries and deaths reported [[1]Present [2]Present [3]]. In 2019, 5333 deaths occurred in the United States, of which 1061 came from the construction industry [4]. Similarly, it was reported that around 50 % of all work -related deaths occur in the construction area in Korea [5]. Many of these incidents are due to the non -compliance of security protocols and uncertain behavior, e.g.PS) or use uncertain tools and procedures [[6]Present [7]Present [8]].

In order to mitigate such risks, camera-based surveillance systems that are integrated into computer vision technologies [9]. These systems offer several advantages, including cost effectiveness, non-intrusiveness and continuous remote monitoring functions. In addition to the security improvement, surveillance cameras have become increasingly important in order to improve two other critical aspects of the performance of the construction project: productivity and quality.

With regard to productivity, monitoring systems enable continuous persecution of labor movements and machine operations and offer valuable insights into the duration of tasks, idle times and the utilization patterns of the equipment. These data streams enable objective ratings of the service, the identification of workflow inefficiencies and the proactive redistribution of resources [10,11]. For quality assurance, high-resolution video feeds of strategically positioned cameras in real-time or after-event inspection of construction activities, material handling and processing enable. This supports the early detection of deviations from the design specifications – e.g. B. improperly [12]. These multifunctional applications position monitoring cameras as a basic element of modern construction management and offer real-time insights into safety, productivity and quality performance indicators.

Despite the growing interest in visual surveillance, the strategic placement of surveillance cameras remains an unsolved challenge. The fixed positioning of CCTV Units often lead to blind spots or redundant cover. It is therefore essential to ensure that cameras are optimally placed on construction sites in order to maintain maximum effectiveness [13]. Monitoring effectiveness can be improved by aligning camera positions with the unique layout, the dangers and the endangered area of ​​a location [14]. This can also improve the general security of a building location and minimize the dependence on experts or school training -dependent staff. Reduce this trust by reducing this trust [9]. This motivates the formalization of the problem of the surveillance camera (problem of the surveillance camera (SCPP) what aims to maximize an effective monitoring cover and at the same time minimize the number of camera and the costs [[13]Present [14]Present [15]].

While SCPP was addressed in various areas, important challenges such as the dynamics of the construction site, the moving costs of the camera and the avoidance of an excessive coverage of irrelevant areas remain largely unresolized or have been investigated in isolation. Additionally, SCPP is classified as a NP-hardness Problem that has caused many researchers to rely on heuristic or metasheuristic algorithms that sacrifice global accuracy for the calculation speed [16,17]. Nevertheless, the proposed linear mixed performance programming (Milp) The wording enables the identification of a global optimum under the defined restrictions and the limited search space. This is with exact solder such as B. reached CplexUse the two-and-bound algorithms to identify the global optimal solution. It is important to emphasize NP-hardness Problems. Rather, we emphasize that the inherent structured nature of our SCPP The wording facilitates precise optimization within realizable computing limits.

To fix these gaps, this study leads a dynamic and cost -conscious optimization framework for a SCPP Based on linear programming of mixed integers (Milp). In contrast to conventional static models, the proposed approach includes the dynamics of the construction phase, the risk -based surveillance priorities and the moving costs of the camera into a uniform mathematical wording. The model ensures globally optimal placement decisions in all project phases, which achieves precise coverage of critical zones and at the same time avoids unnecessary monitoring of irrelevant areas. Remarkably, the inclusion of a relevant cost model offers an economic dimension, the compromises between the installation of new cameras and the reuse of existing compensation, which means that the method for budget-related projects is very practical.

Therefore, this study is directed by the following research question:

How can a monitoring camera system be optimally designed and dynamically pointed out via different construction phases to ensure effective coverage and at the same time minimize the total costs?

This wording offers a robust, yet practical solution for dynamic monitoring planning in construction environments and offers optimal results worldwide that treat both technical and economic dimensions.

The following sections of the paper are organized as follows: Section 2 summarizes existing studies on the applications of surveillance cameras on construction sites and the optimal placement of cameras. Section 3 contains a detailed description of the proposed method. Section 4 examines the proposed method using field data from a construction site. The results are shown in Section 5. In section 6, the results and their effects are discussed. Finally, section 7 concludes the paper with the summary of the main success.