Sistem Deteksi Perhatian Operator Kamera Pengawas Terhadap Monitor Menggunakan Haar Cascade Classifier - Devi Ayu Ratnasari; Hurriyatul Fitriah; Mochammad Hannats Hanafi Ichsan (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Application of security systems to oversee the security of a location using cameras is an alternative that is often chosen. Rather than hiring the services of many security officers, put the cameras in various corners of the place are more economical with less security officer and a camera operator. Job duration of camera operators on average for 6 hours to 8 hours, during this period the operators are required to oversee the monitors in the room. With the long work duration, operator attention is decreased so that the supervision task insufficient. There is no a supervisor for camera operator performance, so operator supervisor technology in real-time is made. By detecting the position of sitting or standing and face detection, then the attention of operators can be detected. Position detection of sitting or standing on this system using HSV color conversion to selecting pixels containing skin color as a value threshold, image Thresholding, and white pixel calculations from the top image and the bottom image. For human face and eyes detection used Haar Cascade Classifier Algorithm. This algorithm can detect the tilt angle of each face up to 15áµ’. The output of the system is the sound of the buzzer, indicates that the operator under the conditions of being out of attention. The system can detect the sit or stand condition with 100% accuracy, for face and eyes detection to determine the condition of attention of 98.33% accuracy.