Claims:WE CLAIM:
1. A method for detecting drowsiness state of a vehicle user (102), the method comprising:
receiving, by a drowsiness detection system (107), one or more current images of the vehicle user (102) from an image capturing device (104) associated with the drowsiness detection system (107) in a current time frame;
determining, by the drowsiness detection system (107), an eye closure ratio of the vehicle user (102) 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 (102) received from a user profile database (115) associated with the drowsiness detection system (107);
normalizing, by the drowsiness detection system (107), 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 (102);
determining, by the drowsiness detection system (107), a Percentage Eye Closure (PEC) value of the vehicle user (102) in the current time frame using the normalized eye closure ratio of the vehicle user (102); and
comparing, by the drowsiness detection system (107), 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 (102).
2. The method as claimed in claim 1, wherein the PEC values of each of the previous time frames is determined by reiterating the steps of determining the eye closure ratio, normalizing the eye closure ratio and determining the PEC value for each of the plurality of previous time frames.

3. The method as claimed in claim 1 further comprises notifying to, by the drowsiness detection system (107), one or more end users the drowsiness state of the vehicle user (102).

4. The method as claimed in claim 1, wherein the profile of the vehicle user (102) is created by:
capturing, by the drowsiness detection system (107), one or more images of eyes and face of the vehicle user (102) using the image capturing device (104), wherein the vehicle user (102) is in a stationary position;
extracting, by the drowsiness detection system (107), plurality of eye parameters and plurality of face parameters from the one or more images;
creating, by the drowsiness detection system (107), the profile of the vehicle user (102) comprising the plurality of eye parameters and the plurality of face parameters; and
storing, by the drowsiness detection system (107), the profile of the vehicle user (102) in the user profile database (115).
5. The method as claimed in claim 4, wherein the plurality of eye parameters comprises average height of the eye, average width of the eye, maximum distance between upper eye lash and eyebrow of the vehicle user (102) and minimum distance between upper eye lash and eyebrow of the vehicle user (102).

6. The method as claimed in claim 4, wherein the plurality of face parameters comprises average height of the face, average width of the face and location of the eye on the face, distance of the face from the image capturing device (104).

7. The method as claimed in claim 1, wherein the one or more eye closure parameters comprises distance between upper eye lash and eyebrow of the vehicle user (102) in the current time frame and distance between lower eye lash and the eyebrow of the vehicle user (102) in the current time frame.

8. The method as claimed in claim 1, wherein the one or more normalization parameters comprises width of face of the vehicle user (102) in the current time frame, height of the face in the current time frame and distance between the face and the image capturing device (104) in the current time frame.

9. The method as claimed in claim 1, wherein determining the PEC value comprises:

determining, by the drowsiness detection system (107), an intermediate-PEC value using the normalized eye closure ratio in the current time frame;

comparing, by the drowsiness detection system (107), the intermediate-PEC value with a dynamically predicted PEC value based on historical data; and

allocating, by the drowsiness detection system (107), the intermediate-PEC value as the PEC value if deviation between the intermediate-PEC value and the dynamically predicted PEC value is within a predefined range.

10. The method as claimed in claim 1, further comprises allocating, by the drowsiness detection system (107), the dynamically predicted PEC value as the PEC value if the deviation between the intermediate-PEC value and the dynamically predicted PEC value exceeds the predefined range.

11. A drowsiness detection system (107) for detecting drowsiness state of a vehicle user (102), the drowsiness detection system (107) comprising:
a processor (109); and
a memory (113) communicatively coupled to the processor (109), wherein the memory (113) stores the processor-executable instructions, which, on execution, causes the processor (109) to:
receive one or more current images of the vehicle user (102) from an image capturing device (104) associated with the drowsiness detection system (107) in a current time frame;
determine an eye closure ratio of the vehicle user (102) 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 (102) received from a user profile database (115) associated with the drowsiness detection system (107);
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 (102);
determine a Percentage Eye Closure (PEC) value of the vehicle user (102) in the current time frame using the normalized eye closure ratio of the vehicle user (102); 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 (102).
12. The drowsiness detection system (107) as claimed in claim 11, wherein the processor (109) determines the PEC values of each of the plurality of previous time frames by reiterating the steps of determining the eye closure ratio, normalizing the eye closure ratio and determining the PEC value for each of the plurality of previous time frames.

13. The drowsiness detection system (107) as claimed in claim 11, wherein the processor (109) further notifies to one or more end users, the drowsiness state of the vehicle user (102).

14. The drowsiness detection system (107) as claimed in claim 11, wherein to create the profile of the vehicle user (102), the instructions cause the processor (109) to:
capture one or more images of eyes and face of the vehicle user (102) using the image capturing device (104), wherein the vehicle user (102) is in a stationary position;
extract plurality of eye parameters and plurality of face parameters from the one or more images;
create the profile of the vehicle user (102) comprising the plurality of eye parameters and the plurality of face parameters; and
store the profile of the vehicle user (102) in the user profile database (115).
15. The drowsiness detection system (107) as claimed in claim 14, wherein the plurality of eye parameters comprises average height of the eye, average width of the eye, maximum distance between upper eye lash and eyebrow of the vehicle user (102) and minimum distance between upper eye lash and eyebrow of the vehicle user (102).

16. The drowsiness detection system (107) as claimed in claim 14, wherein the plurality of face parameters comprises average height of the face, average width of the face and location of the eye on the face, distance of the face from the image capturing device (104).

17. The drowsiness detection system (107) as claimed in claim 11, wherein the one or more eye closure parameters comprises distance between upper eye lash and eyebrow of the vehicle user (102) in the current time frame and distance between lower eye lash and the eyebrow of the vehicle user 102 in the current time frame.

18. The drowsiness detection system (107) as claimed in claim 11, wherein the one or more normalization parameters comprises width of face of the vehicle user (102) in the current time frame, height of the face in the current time frame and distance between the face and the image capturing device (104) in the current time frame.

19. The drowsiness detection system (107) as claimed in claim 11, wherein to determine the PEC value, the instructions cause the processor (109) to:

determine an intermediate-PEC value using the normalized eye closure ratio in the current time frame;

compare the intermediate-PEC value with a dynamically predicted PEC value based on historical data; and

allocate the intermediate-PEC value as the PEC value if deviation between the intermediate-PEC value and the dynamically predicted PEC value is within a predefined range.

20. The drowsiness detection system (107) as claimed in claim 21, wherein the processor (109) is further configured to allocate the dynamically predicted PEC value as the PEC value if the deviation between the intermediate-PEC value and the dynamically predicted PEC value exceeds the predefined range.

Dated this 07th day of March 2017

SWETHA SN
OF K & S PARTNERS
AGENT FOR THE APPLICANT
, Description:TECHNICAL FIELD
The present subject matter relates generally to video analytics, and more particularly, but not exclusively to a method and a system for detecting drowsiness state of a vehicle user.