The document discusses the significance of drowsiness detection in drivers, highlighting its impact on road safety and accident rates. It outlines various techniques for detecting drowsiness, including behavioral, vehicular, and physiological parameters, as well as the use of digital image processing. The proposed system architecture consists of modules for face and eye detection, tracking, and drowsiness and distraction detection.

1. DROWSINESS DETECTION
- SAURABH NITIN KAWLI
MENTOR : AVANTIKA MA’AM
- LALITKUMAR KANARAMJI CHOUDHARY
MENTOR : KHYATI MA’AM

2. - Drowsiness of the drivers is one of the key issues for majority of
road accidents.
- Drowsiness threatens the road safety and causes severe
injuries sometimes, resulting in fatality of the victim and
economical losses.
WHAT
IS
DROWSINESS???

3. APPROACHES TO DETECTING DROWSINESS
• BEHAVIOURAL PARAMETERS-BASED TECHNIQUES
• VEHICULAR PARAMETERS-BASED TECHNIQUES
• PHYSIOLOGICAL PARAMETERS-BASED TECHNIQUES
• DIGITAL IMAGE PROCESSING

4. • BEHAVIOURAL PARAMETERS-BASED TECHNIQUES
 Measuring the driver’s fatigue without using non-invasive
instruments comes under this category.
 Analysing the behaviour of the driver based on his/her eye
closure ratio, blink frequency, yawning, position of the head
and facial expressions.

5. • VEHICULAR PARAMETERS-BASED TECHNIQUES
 Measuring the fatigue nature of the driver through vehicle
driving patterns comes under this category.
 These parameters include lane changing patterns, steering
wheel angle, steering wheel grip force, vehicle speed
variability and many more.

6. • PHYSIOLOGICAL PARAMETERS-BASED TECHNIQUES
 Measuring the drowsiness of the driver based on the
physical conditions of the driver fall under this category.
 Such parameters may be respiration rate, heart-beat rate,
body temperature and many more.

7. • DIGITAL IMAGE PROCESSING
 The term digital image processing generally refers to
processing of a two- dimensional picture by a digital
computer.
 3 Types :
 Pixel
 Digital Image
 Gray Level

8. • Parameters for determining the drowsiness
• Eye blink
• Area of the pupils detected at eyes
• Yawning

9. ARCHITECTURE
OF THE
DROWSINESS
DETECTION
SYSTEM

10. ARCHITECTURE
CONSISTS OF 6
MODULES:
• Face Detection
• Eye Detection
• Face Tracking
• Eye Tracking
• Drowsiness Detection
• Distraction Detection

11. Screenshots

12. Screenshots

13. • Usage Of Drowsiness Detection
Tesla
Self-driving cars
Artificial Intelligence (AI)

14. DROWSINESS
DETECTOR
IN CAR

15. ANY QUESTIONS ???

16. THANK
YOU..!!!