Road transport systems are an essential part of human transit between places. The sensation of sleep reduces the driver's level of vigilance, producing dangerous situations and increasing the probability of accidents. There are different steps to make this system fully functional. 1. Detection Stage 2. Tracking Stage 3. Image Classification 4. Warning Stage We analyze both the driver's face and eyes to measure the percentage of eye closure. If drowsiness is detected, the alarm rings. If not, it captures the picture. Some of the libraries can help you to create a customized drowsiness detection system.
This is the initial stage of the system. Every time the system is started, it needs to be set up and optimized for current user and conditions. Setup steps include: collecting a set of open/closed eyes samples, along with the driver’s normal head position.
Java 1 Version:Current License: Strong Copyleft (GPL-3.0)
Python 616 Version:Current License: Strong Copyleft (GPL-3.0)
The key step is close monitoring and getting information from image classification libraries about driver's eyes. The system determines whether the eyes are still closed or open or the relative position. These additional computations are required to improve the system’s ability to determine whether the driver is drowsy or not.
Python 30 Version:Current License: Permissive (MIT)
Python 32 Version:Current License: Permissive (MIT)
The system enters the regular tracking (monitoring) stage when the driver’s head and eyes are correctly located; the system enters the standard tracking (monitoring) stage. An essential step in this stage is continuously monitoring the driver’s eyes within a dynamically allocated tracking area.
Python 142 Version:Current License: Permissive (BSD-3-Clause)
C++ 924 Version:Current License: No License
Python 25807 Version:Current License: Permissive (Apache-2.0)
Python 1204 Version:Current License: Permissive (MIT)