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This paper proposes an alternate method to obtain position and orientation of multiple soccer robots in global vision system. The proposed method uses high-angle shot of camera as a substitute to bird-eye angle, and employs a specially designed robot markers that contain cues for the detection of the position, orientation, and IDs of the robots. Perspective transformation to normalize the perspective arena image obtained from high-angle shot is utilized in this method. Three testing phase, namely unit testing, integration testing, and system testing are conducted. The results of unit testing are: four arenas are detected from four different placements in the room, the robot detection has an error of 0.86%, and ball detection has an error of 0.4%. Integration testing obtains the global orientation of the robot, which has an error of 1.1%. From the result of system testing, the best camera resolution is 360p with 50% minimum camera brightness. The result of system testing also proves that the application system can be implemented in different rooms and with different camera positions.  Index Terms-robot soccer, global vision, image processing, border tracing, perspective transformation, high-angle shot B. Object Detection in Robot Soccer Detection of objects in robot soccer vision system usually consists of two steps: cue detection and feature

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The university as an educational institution can apply technology in the campus environment. Currently, the security system for office space that is integrated with digital data has been somewhat limited. The main problem is that office space security items are not guaranteed as there might be outsiders who can enter the office. Therefore, this study aims to develop a system using biometric (face) recognition based on Viola-Jones and Radial Basis Function Network (RBFN) algorithm to ensure office room security. Based on the results, the system developed shows that object detection can work well with an object detection rate of 80%. This system has a pretty good accuracy because the object matching success is 73% of the object detected. The final result obtained from this study is a prototype development for office security using face recognition features that are useful to improve safety and comfort for occupants of office space (due to the availability of access rights) so that not everyone can enter the office.