Human Face Counting using OpenCV

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Description

Human Face Counting using OpenCV

It is observed that the traffic accidents are mostly sourced by the drivers and one of the main reasons is the lack of attention. Distracted drivers can’t observe the traffic flow and traffic accidents occur as a result thereof. Brainwaves produce weak electrical signals that can be measured from the skull. Electroencephalography is a system that measures the activity of brainwaves using an electrical method. The Brain Computer Interface is a system that converts electrophysiological actions or metabolic rates to signals to be interpreted by a device. In the last decades, brain signals could be measured with systems requiring high costs, but nowadays, many EEG devices are available for personal use. These EEG devices and systems have their signal transformation methods. In this paper, a mobile system design that detects the distractions of drivers that are the major factor in traffic accidents via EEG signals is proposed. This system provides the necessary warnings to the driver. The proposed system is aimed to measure and analyze the attention and meditation status via the brain signals of the drivers. In case of the drivers’ attention has been dispersed, it is aimed to provide audio alerts to the drivers. It is proposed to use NeuroSky Mindwave Mobile as an EEG device because of the wireless and easy-to-use options.


Human Face Counting using OpenCV

Existing System

Steering Wheel Movement (SWM)?is measured using a steering angle sensor and it is a widely used vehicle-based measure for detecting the level of driver drowsiness. Using an angle sensor mounted on the steering column, the driver’s steering behavior is measured. When drowsy, the number of micro-corrections on the steering wheel reduces compared to normal driving. Fairclough and Graham found that sleep-deprived drivers made fewer steering wheel reversals than normal drivers. To eliminate the effect of lane changes, the researchers considered only small steering wheel movements (between 0.5? and 5?), which are needed to adjust the lateral position within the lane. Hence, based on small SWMs, it is possible to determine the drowsiness state of the driver and thus provide an alert if needed. In a simulated environment, light side winds that pushed the car to the right side of the road were added along a curved road in order to create variations in the lateral position and force the drivers to make corrective SWMs. Car companies, such as Nissan and Renault, have adopted SWMs but it works in very limited situations. This is because they can function reliably only in particular environments and are too dependent on the geometric characteristics of the road and to a lesser extent on the kinetic characteristics of the vehicle.


Proposed System:

In this paper, a mobile system design that detects the distractions of drivers that are the major factor in traffic accidents via EEG signals is proposed. This system provides the necessary warnings to the driver. The proposed system is aimed to measure and analyze the attention and meditation status via the brain signals of the drivers. In case of the drivers’ attention has been dispersed, it is aimed to provide audio alerts to the drivers.

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