Car Driver Skills
Assessment using
Posture Recognition
Presented By: sumit kadyan
Authors
• Madalina –Ioana Toma (Transilvania
University of Brasov)
• Leon J.M. Rothkrantz (Delft University of
technology)...
Need For it
• Difficult to learn driving in real life
scenario
• Safety issues
• Time boundation
• Learning driving as a N...
Introduction
• Recognition of driving posture with High
Accuracy
• Feedback mechanism for novice drivers using
Alarm syste...
What Sets it apart?
• Recognition of complete body parts.
• Use of “Markerless” Sensors.
• Provides accurate measurement o...
Simulation Environment
framework
KINECT EYELINK2 CLIPS
LOGITECH
G27
TORCS PC
Pictorial Representation
Framework Components
• Kinects-sensing upper body movements
• Torcs-3D car simulator
• Clips-For rule based expert system
...
Markerless Sensor
Markerless Sensor
• Uses pattern recognition principle
• Monitor process quality via control panel
or via Ethernet
• Repro...
KINECT
KINECT Sensor
• RGB camera sensor
• Configuration is done using Sdk tool by
windows
• IR Emitter and IR depth Sensor
• Use...
EYELINK II
Eye link 2
• High resolution and data rate
• Head mounted video-based eye tracker.
• Used for tracking eyes movement and
h...
CLIPS
• C Language Integrated Production System.
• CLIPS incorporates a complete object-
oriented language(COOL) for writi...
Logitech G27
• Provides simulation Environment with TORCS.
• USB Interface.
TORCS virtual environment
TORCS
• 3D car simulator supporting input devices(
steering wheels, joystick, game pads etc.)
• Provides connection, confi...
Related Work
• Pose Estimation
• Gaze Detection
• Focused only on Expert Drivers.
• Analyses done using offline techniques...
How it Works?
• Takes real time parameters from sensors
and environment.
• Refers to an expert rule based system to
determ...
Description of the rules
Defining Rules
• Rules for recognizing driving postures are
stored in the knowledge base system.
• Rules for driving postu...
Working
• Each group represnts a postuers runsin
paralles with the other
• A driver posture is represnted a key poses
• Wh...
Finite state machine
diagram
DFSM
• Determisnntic finite state machine
• , S, s0 , , F
• -Input alphabet(from the sensors)
• S-Finite set of states (sh...
Diagram of the interface
Experiment
• Experiment was focused on developing a
assistive intelligent system for indoor training
of novice drivers
• E...
Two Experiments
Experiments
Conducted
Detecting Robustness
and accuracy of
posture recognition
for novice drivers
Complete...
Participants
• 12 participants
• 8 males and 4 females
• All having driver license
• With little or no experience
Results of Experiment 1
• Every subject performed the postures for
10 times
• Driving postures recognition rate achieves
9...
Table of Results
Experiment 2 : Rules
• driver needs to start the car (StC)
• driver wants to drive away (DA)
• driver keeps the lane (KL)
...
Results of Experiment 2
• In the StC situation we achieved 88%
correct postures detectioni
• In the IS and DS speed variat...
Results experiment 2
• In the StC situation we achieved 88%
correct postures detection.
• In the IS and DS speed variation...
Table of Results
Conclusion
• To improve the take over and forward
parking by combining probabilistic
methods reducing uncertainty of certa...
References
• Toma, Madalina-Ioana; Rothkrantz, Leon J.M.; Antonya, Csaba, "Car driver
skills assessment based on driving p...
Human Computer Intreaction
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Human Computer Intreaction

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About a car simulator research study paper that talks about the finding how car simulators make life easy for novice drivers.

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Human Computer Intreaction

  1. 1. Car Driver Skills Assessment using Posture Recognition Presented By: sumit kadyan
  2. 2. Authors • Madalina –Ioana Toma (Transilvania University of Brasov) • Leon J.M. Rothkrantz (Delft University of technology) • Csaba Antonya (M.I.Toma)
  3. 3. Need For it • Difficult to learn driving in real life scenario • Safety issues • Time boundation • Learning driving as a Novice is a key in driving career
  4. 4. Introduction • Recognition of driving posture with High Accuracy • Feedback mechanism for novice drivers using Alarm system. • Experiment conducted in real time.
  5. 5. What Sets it apart? • Recognition of complete body parts. • Use of “Markerless” Sensors. • Provides accurate measurement of joint configuration and rapid movements of hands.
  6. 6. Simulation Environment framework KINECT EYELINK2 CLIPS LOGITECH G27 TORCS PC
  7. 7. Pictorial Representation
  8. 8. Framework Components • Kinects-sensing upper body movements • Torcs-3D car simulator • Clips-For rule based expert system • Eyelink 2 device –for sensing eye and gaze movement
  9. 9. Markerless Sensor
  10. 10. Markerless Sensor • Uses pattern recognition principle • Monitor process quality via control panel or via Ethernet • Reproducibility of 0.6 mm • Plug can be rotated 90° • High scanning speed of 7 m/s
  11. 11. KINECT
  12. 12. KINECT Sensor • RGB camera sensor • Configuration is done using Sdk tool by windows • IR Emitter and IR depth Sensor • Used for tracking upper body movements
  13. 13. EYELINK II
  14. 14. Eye link 2 • High resolution and data rate • Head mounted video-based eye tracker. • Used for tracking eyes movement and head orientation • Two eye cameras allow binocular eye tracking
  15. 15. CLIPS • C Language Integrated Production System. • CLIPS incorporates a complete object- oriented language(COOL) for writing expert systems. • COOL combines the programming paradigms of procedural, object oriented and logical (theorem proving) languages. • Provides High Portability.
  16. 16. Logitech G27 • Provides simulation Environment with TORCS. • USB Interface.
  17. 17. TORCS virtual environment
  18. 18. TORCS • 3D car simulator supporting input devices( steering wheels, joystick, game pads etc.) • Provides connection, configuration and synchronization. • Written in C++ and open source avaliable under GPL license • Easy to add/create content • Excellent performance and stability
  19. 19. Related Work • Pose Estimation • Gaze Detection • Focused only on Expert Drivers. • Analyses done using offline techniques like silhouettes, bounding boxes.
  20. 20. How it Works? • Takes real time parameters from sensors and environment. • Refers to an expert rule based system to determine the driving postures and give feedback ,also sound an alarm if the novice driver posture is wrong. • Uses the clips inference engine • Matching takes place between current state of fact list and list of instances
  21. 21. Description of the rules
  22. 22. Defining Rules • Rules for recognizing driving postures are stored in the knowledge base system. • Rules for driving posture:  DP1,DP2,DP3,DP4  DP1-Left hand postures  DP2-Right hand postures  DP3-Eye and Head postures  DP4-leg postures
  23. 23. Working • Each group represnts a postuers runsin paralles with the other • A driver posture is represnted a key poses • Which is a combination of 2- 5 key poses • These are the inputs to the CLIPS • In a driving task the driving posture used to perform that maneuver are defined in a specific order
  24. 24. Finite state machine diagram
  25. 25. DFSM • Determisnntic finite state machine • , S, s0 , , F • -Input alphabet(from the sensors) • S-Finite set of states (showing transition in DP1 ,DP2 …DP4 • s0- Initial state(When the system is calibrated for start ) • -state transition(from one • F-final state
  26. 26. Diagram of the interface
  27. 27. Experiment • Experiment was focused on developing a assistive intelligent system for indoor training of novice drivers • Experiments conducted in laboratory with proper lighting for sensors • 2 kind of experiments • One for robustness and performance of posture recognition the novice driver without traffic • 2 in is the complete framework evaluation.
  28. 28. Two Experiments Experiments Conducted Detecting Robustness and accuracy of posture recognition for novice drivers Complete framework evaluation and provide feedback
  29. 29. Participants • 12 participants • 8 males and 4 females • All having driver license • With little or no experience
  30. 30. Results of Experiment 1 • Every subject performed the postures for 10 times • Driving postures recognition rate achieves 96.4% accuracy • Driving posture stability achieves 96.21% accuracy • GOOD” and “WORST” messages
  31. 31. Table of Results
  32. 32. Experiment 2 : Rules • driver needs to start the car (StC) • driver wants to drive away (DA) • driver keeps the lane (KL) • driver increases the speed (IS) or decreases the speed (DS) based on traffic signs • driver wants to take over (TO) or change lane (CL) • driver wants to make a forward parking (FP) driver wants to stop the car (SpC).
  33. 33. Results of Experiment 2 • In the StC situation we achieved 88% correct postures detectioni • In the IS and DS speed variation situations we achieved an accuracy of 100%. • A lower accuracy of less than 70% we obtained in TO and FP
  34. 34. Results experiment 2 • In the StC situation we achieved 88% correct postures detection. • In the IS and DS speed variation situations we achieved an accuracy of 100%. • A lower accuracy of less than 70% we obtained in TO and FP
  35. 35. Table of Results
  36. 36. Conclusion • To improve the take over and forward parking by combining probabilistic methods reducing uncertainty of certain driver postures.
  37. 37. References • Toma, Madalina-Ioana; Rothkrantz, Leon J.M.; Antonya, Csaba, "Car driver skills assessment based on driving postures recognition," Cognitive Infocommunications (CogInfoCom), 2012 IEEE 3rd International Conference on , vol., no., pp.439,446, 2-5 Dec. 2012 • I. Lefter, L.J.M. Rothkrantz, P. Bouchner, P. Wiggers: “A multimodal car driver surveillance system in a military area”, Driver Car Interaction & Interface, 2010. • Y.F. Lu, and Ch.Li: “Recognition of Driver Turn Behavior Based on Video Analysis”, Journal of Advanced Materials Research Vol. 433-44, pp 6230- 6234, 2012. • D.B. Kaber, Y. Liang, Y. Zhang, M. L. Rogers, and S. Gangakhedkar: “Driver performance effects of simultaneous visual and cognitive distraction and adaptation behavior”, Journal of Transportation Research Part F 15, pp. 491– 501, 2012.

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