Pattern analysis of risk situations using multi-sensor

Background/Objectives: Existing crime prevention systems manually monitor risk situations using street lights, CCTVs, and security equipment. Since there are many areas where the workforce is held responsible, it is difficult to closely manage all systems due to work overload.

Methods/Statistical analysis: The data of maps constructed and continuously updated through the system will allow more accurate predictions of crime and contribute to crime prevention strategies. Furthermore, replacing existing patrol manpower to unmanned drones will allow for more efficient human resources management as well as contribute to the crime prevention infrastructure, thereby minimizing the existence of blind spots in the current system.

Findings: It is not easy to diffuse the initial situation in the case of an emergency through prompt notifications. Therefore, a low-cost, integrated management system is needed to prevent major accidents and to minimize damage by detecting crime and fire risks in the early stage. It will be easier to judge risks if we use the multi-sensor and pattern analysis algorithms proposed in this study. Occurrences of crime and fire have been rapidly rising with the quick pace of industrialization. This has resulted in an increase of unease among citizens as well as a rising demand for security and safety in residential environments. As the times change, it is necessary to develop advanced science technology that can predict crimes in order to construct crime preventing environments. The Risk Notification Service can promptly respond to the current status and situation of the user by forwarding the status to the administrator or guardian. Police activity can be strengthened by building a high-tech science and security system to monitor areas susceptible to crime in real-time.

Improvements/Applications: This study looks into problems of the existing monitoring system and proposes an integrated control system for crime prevention.

 

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Background/Objectives: Existing crime prevention systems manually monitor risk situations using street lights, CCTVs, and security equipment. Since there are many areas where the workforce is held responsible, it is difficult to closely manage all systems due to work overload.

Methods/Statistical analysis: The data of maps constructed and continuously updated through the system will allow more accurate predictions of crime and contribute to crime prevention strategies. Furthermore, replacing existing patrol manpower to unmanned drones will allow for more efficient human resources management as well as contribute to the crime prevention infrastructure, thereby minimizing the existence of blind spots in the current system.

Findings: It is not easy to diffuse the initial situation in the case of an emergency through prompt notifications. Therefore, a low-cost, integrated management system is needed to prevent major accidents and to minimize damage by detecting crime and fire risks in the early stage. It will be easier to judge risks if we use the multi-sensor and pattern analysis algorithms proposed in this study. Occurrences of crime and fire have been rapidly rising with the quick pace of industrialization. This has resulted in an increase of unease among citizens as well as a rising demand for security and safety in residential environments. As the times change, it is necessary to develop advanced science technology that can predict crimes in order to construct crime preventing environments. The Risk Notification Service can promptly respond to the current status and situation of the user by forwarding the status to the administrator or guardian. Police activity can be strengthened by building a high-tech science and security system to monitor areas susceptible to crime in real-time.

Improvements/Applications: This study looks into problems of the existing monitoring system and proposes an integrated control system for crime prevention.