Uno de los factores importantes que influyen en los accidentes automovilísticos es el manejar bajo condiciones no-óptimas, tales como estrés, ira, miedo, depresión entre otros, en las cuales la posibilidad de sufrir un accidente durante manejo se incrementa. Por lo tanto, hasta la fecha han sido propuestos varios esquemas que detectan la emoción del conductor basado en su expresión facial. La mayoría de ellos usan solo un fotograma (una imagen) y operan en condiciones restringidas que rara vez se presentan en condiciones reales de manejo. Con el fin de poder resolver este problema, este artículo presenta un algoritmo para el reconocimiento de la emoción del conductor basado en sus expresiones faciales, en el cual a partir de la secuencia de cuadros de video se extraen vectores de características temporales usando los puntos relevantes del rostro. Los vectores de características extraídos se introducen en diferentes clasificadores, tales como Maquina de Soporte Vectorial (SVM) y K-vecinos más cercanos (KNN) para la comparación de su funcionamiento.
In this paper, we propose a portable device named SOMN_IA, to detect drowsiness and distraction in drivers. The SOMN_IA can be installed inside of any type of vehicle, and it operates in real time, alerting the dangerous state caused by drowsiness and/or distraction in the driver. The SOMN_IA contains three types of alarm: light alarm, sound alarm, and the transmission of information about the driver’s dangerous state to a third party if the driver does not correct his/her dangerous state. The SOMN_IA contains a face detector and a classifier based on the convolutional neural networks (CNN), and it aids in the management of consecutive information, including isolated error correction mechanisms. All of the algorithmic parts of the SOMN_IA are analyzed and adjusted to operate in real-time in a portable device with limited computational power and memory space. The SOMN_IA requires only a buck-type converter to connect to the car battery. The SONM_IA discriminates correctly between real drowsiness and normal blinking, as well as between real dangerous distraction and a driver’s normal attention to his/her right and left. Although the real performance of the SOMN_IA is superior to the CNN classification accuracy thanks to isolated error correction, we compare the CNN classification accuracy with the previous systems.
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