Armed and Partially Covered Face Related Robberies Alerting System Using Computer Vision
Abstract
Robbery is the completed or attempted theft, directly from a person, of property or cash by force or threat of force, with or without a weapon, and with or without injury. Armed robbery is a serious crime that can traumatically that emotionally and mentally profoundly traumatize its victims. Armed robbery is usually motivated by a desire to acquire money, which is then commonly used to buy drugs. [1] However, some armed robbers are associated with the crime. [2] In the current decade, armed robbery is one of the major issues in society. According to Statista research department, there were more than 0.25 million armed robberies happened in the USA in 2018. [3] Moreover, these robberies accounted for an estimated $438 million in losses. [2] . Moreover is the USA there were more than 10000 murders, victims, by weapons in 2018. [4]When analyzing most of these armed robberies have happened locations are Banks, Gas or Service stations, and commercial houses in the USA. To monitor and act accordingly to the issue still, there is no proper method. Present in most places there are several security officers to monitor these robberies within 24H using CCTV cameras.
In this research, the authors propose a novel approach to prevent this issue using a computer vision-based armed robberies alerting system. Here this system is able to detect the weapons of the robbers. Since most of the time robbers come with partially covered faces. In this research, the proposed system is able to detect partially covered faces also. When the system identified weapons or a partially covered face in a bank or a commercial house, it will send an alert by notifying the risk of the robbery to the in house security officers and relevant authorities. The proposed solution used an object detection model and a facial landmark identification based approach to detect robbers. Because of this system, no longer the security officers need to monitor CCTV cameras by themselves
References
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