Deep convolutional network based real time fatigue detection and drowsiness alertness system

Abstract

<span>Fatigue and drowsiness detection techniques based on the external features are under progress, and the methods of facial feature extraction require further development. This paper discusses the innovative processes, efficient methods, and recent advancements in the field of drowsiness and fatigue detection. In this proposed model, a wide application is planned in the field of artificial intelligence by defining the fundamentals of human-computer interaction, facial expression recognition and driver fatigue-sleepiness determination. This research outlines an efficient and effective three-phase strategy for detecting drowsiness. Viola Jones is used to detect facial traits in these three phases. Detection of yawning and tracking once the face has been identified, the segmenting the skin, the system becomes lighting invariant portion by itself, focusing on the chromatic components based on skin, and to reject most of non-face image backdrops. The color eye tracking and yawning detection are carried out by template matching with the correlation coefficient. The vectors of features based on each of the above phases is concatenated, and a binary result is obtained. The analysis of sound and successive frames into fatigue and non-fatigue states has been classified. If the time in fatigue state exceeds the threshold, the system will sound an alarm. </span>

Identifikasi Tingkat Kesadaran Pengemudi dari Data Video dengan Menggunakan Convolutional Long Short Term Memory

“…Pada penelitian [2] identifikasi kelelahan pengemudi dengan menggunakan pengenalan iris menghasilkan akurasi sekitar 80%. Pada penelitian [4] deteksi kelelahan dan kantuk menggunakan deep convolutional network menghasilkan rata-rata akurasi sebesar 68%, 81%, 91%, dan 93% pada skenario yang berbeda. Pada penelitian [5] deteksi kantuk pengemudi menggunakan analisis electrooculogram dan optimasi grey wolf menghasilkan akurasi 99,6%.…”