Method and a system for detecting drowsiness state of a vehicle user

A drowsiness detection system for detecting drowsiness state of a vehicle user, the drowsiness detection system comprising:

• a processor;
• and
• a memory communicatively coupled to the processor, wherein the memory stores the processor-executable instructions, which, on execution, causes the processor to:
• receive one or more current images of the vehicle user from an image capturing device associated with the drowsiness detection system in a current time frame;
• determine an eye closure ratio of the vehicle user in the current time frame using one or more eye closure parameters extracted from the one or more current images in real-time and a profile of the vehicle user received from a user profile database associated with the drowsiness detection system;
• normalize the eye closure ratio using a scaling factor computed in real-time, wherein the scaling factor is computed using one or more normalizing parameters extracted from the one or more current images in real-time and the profile of the vehicle user;
• determine a Percentage Eye Closure (PEC) value of the vehicle user in the current time frame using the normalized eye closure ratio of the vehicle user;
• and
• compare the PEC value of the current time frame and PEC values of plurality of previous time frames with a predefined threshold to detect drowsiness state of the vehicle user.

A non-transitory computer readable medium including instructions stored thereon that when processed by at least one processor causes a drowsiness detection system to perform operations comprising:

• receiving one or more current images of a vehicle user from an image capturing device associated with the drowsiness detection system in a current time frame;
• determining an eye closure ratio of the vehicle user in the current time frame using one or more eye closure parameters extracted from the one or more current images in real-time and a profile of the vehicle user received from a user profile database associated with the drowsiness detection system;
• normalizing the eye closure ratio using a scaling factor computed in real-time, wherein the scaling factor is computed using one or more normalizing parameters extracted from the one or more current images in real-time and the profile of the vehicle user;
• determining a Percentage Eye Closure (PEC) value of the vehicle user in the current time frame using the normalized eye closure ratio of the vehicle user;
• and
• comparing the PEC value of the current time frame and PEC values of plurality of previous time frames with a predefined threshold to detect drowsiness state of the vehicle user.