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M.S. Student, Heejong Hong 07. 14. 2014

Estimating the Driving State of Oncoming Vehicles From a Moving Platform Using Stereo Vision IEEE Intelligent Transportation Systems 2009. Alexander Barth and Uwe Franke. M.S. Student, Heejong Hong 07. 14. 2014. Outline. Introduction Related Works Proposed Method Experimental Results

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M.S. Student, Heejong Hong 07. 14. 2014

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  1. Estimating the Driving State of Oncoming Vehicles From a Moving Platform Using Stereo VisionIEEE Intelligent Transportation Systems 2009 Alexander Barth and Uwe Franke M.S. Student, Heejong Hong 07. 14. 2014

  2. Outline • Introduction • RelatedWorks • Proposed Method • ExperimentalResults • Conclusion

  3. Introduction • Driver-assistance and safety systems Dynamic Object Detection for DAS Safety System with Dynamic Path Estimation http://www.6d-vision.com/home/bedeutung

  4. Related Works • A model-free object representation based on groups • Fusion active sensors • Track-before-detection • Rediscovering an image region labeled as vehicle D. Beymer , P. McLauchlan , B. Coifman and J. Malik  "A real-time computer vision system for measuring traffic parameters",  Proc. Comput. Vis. Pattern Recog,  pp.495 -501 1997  M. Maehlisch , W. Ritter and K. Dietmayer  "De-cluttering with integrated probabilistic data association for multisensormultitarget ACC vehicle tracking",  Proc. IEEE Intell. Veh. Symp.,  pp.178 -183 2007  U. Franke , C. Rabe , H. Badino and S. Gehrig  "6D-vision: Fusion of stereo and motion for robust environment perception",  Proc. 27th DAGM Symp.,  pp.216 -223 2005  X. Li , X. Yao , Y. Murphey , R. Karlsen and G. Gerhart  "A real-time vehicle detection and tracking system in outdoor traffic scenes",  Proc. 17th Int. Conf. Pattern Recog.,  pp.II:761 -II:764 2004  1. 2. 3.

  5. Proposed Method

  6. Object Model • Pose (relative orientation and translation to ego-vehicle) • Motion State (velocity, acceleration, yaw rate) • Shape (rigid 3-D point cloud) Pose Motion State Shape

  7. Object Tracking • Extended Kalman Filter (EKF): Kalman filter for nonlinear model Example) State transition(f) and observation model(h) Discrete-time predict and update equations Jacobian of system & measurement model Wikipedia : http://en.wikipedia.org/wiki/Extended_Kalman_filter

  8. Object Tracking 1. State Vector of an object instance Reference point in ego-coordinates Rotation point in object-coordinates The object origin is ideally defined on the center rear axis

  9. Object Tracking 2. Dynamic(System) Model Predicted state vector Time-discrete system Equation Transformation of an object point Translation matrix Ris 3x3 rotation matrix around the height axis N. Kaempchen , K. Weiss , M. Schaefer and K. Dietmayer  "IMM object tracking for high dynamic driving maneuvers",  Proc. IEEE Intell. Veh. Symp.,  pp.825 -830 2004 

  10. Object Tracking 3. Measurement Model Objects feature points on image coordinates using feature tracker (KLT) Feature point tracking using KLT The measurement nonlinear eq. : perspective camera model Jacobian of measurement model

  11. Kalman Filter Initialization • Image Based Initialization • Radar-Based Initialization(detect oncoming vehicle up to 200m) The centroid of the 3-D positions : The mean velocity vector : Initial Yaw : The lateral and longitudinal positions of the radar target : , Absolute radar velocity of the object : Initial Yaw :

  12. Point Model Update t • Maximum-likelihood estimation • Simple average filter Object’s Shape Expectation = 3x3 covariance matrix of Expected object’s shape

  13. Experimental Result

  14. Simulation Results • Synthetic Sequence

  15. Real World Results • Country Road Curve I

  16. Real World Results • Country Road Curve II

  17. Real World Results • Oncoming Traffic at Intersections

  18. Real World Results • Leading Vehicles & Partial Occlusions

  19. Real World Results • Challenges and Limits

  20. Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013 Conclusion

  21. Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013 Conclusion • Contribution • New method for the image-based real-time tracking (25Hz, 640x480) • Results of experiments with synthetic data & real-world • Two different object detection method (image & radar) • Feature-based object point model does not require a priori knowledge about the object’s shape • Weakness • No specific system block diagram • User defined rotation point • Shape depends on outlier removing algorithm (ex : max distance parameter) • Shape is very sensitive about outlier of point cloud(because of yaw)

  22. Thank you!

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