Estimating the Driving State of Oncoming Vehicles From a Moving Platform Using Stereo Vision IEEE...
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- Slide 1
- Estimating the Driving State of Oncoming Vehicles From a Moving
Platform Using Stereo Vision IEEE Intelligent Transportation
Systems 2009 M.S. Student, Heejong Hong 07. 14. 2014 Alexander
Barth and Uwe Franke
- Slide 2
- Introduction Related Works Proposed Method Experimental Results
Conclusion Outline 2
- Slide 3
- Introduction Driver-assistance and safety systems
http://www.6d-vision.com/home/bedeutung Dynamic Object Detection
for DAS Safety System with Dynamic Path Estimation
- Slide 4
- 1. A model-free object representation based on groups 2. Fusion
active sensors 3. Track-before-detection 4. Rediscovering an image
region labeled as vehicle Related Works 4 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
multisensor multitarget 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.
- Slide 5
- 5 Proposed Method
- Slide 6
- Object Model 1. Pose (relative orientation and translation to
ego-vehicle) 2. Motion State (velocity, acceleration, yaw rate) 3.
Shape (rigid 3-D point cloud) Motion State Shape Pose
- Slide 7
- Object Tracking Extended Kalman Filter (EKF) : Kalman filter
for nonlinear model State transition(f) and observation model(h)
Discrete-time predict and update equations Wikipedia :
http://en.wikipedia.org/wiki/Extended_Kalman_filter Jacobian of
system & measurement model Example)
- Slide 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
- Slide 9
- Object Tracking 2. Dynamic(System) Model R is 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
Predicted state vector Time-discrete system EquationTransformation
of an object point Translation matrix
- Slide 10
- Object Tracking 3. Measurement Model The measurement nonlinear
eq. : perspective camera model Jacobian of measurement model
Objects feature points on image coordinates using feature tracker
(KLT) Feature point tracking using KLT
- Slide 11
- Kalman Filter Initialization 1. Image Based Initialization 2.
Radar-Based Initialization (detect oncoming vehicle up to 200m) The
mean velocity vector : The centroid of the 3-D positions : Initial
Yaw : The lateral and longitudinal positions of the radar target :,
Absolute radar velocity of the object : Initial Yaw :
- Slide 12
- Point Model Update 1. Maximum-likelihood estimation 2. Simple
average filter Objects Shape Expectation = 3x3 covariance matrix of
t Expected objects shape
- Slide 13
- 13 Experimental Result
- Slide 14
- Simulation Results Synthetic Sequence
- Slide 15
- Real World Results Country Road Curve I
- Slide 16
- Real World Results Country Road Curve II
- Slide 17
- Real World Results Oncoming Traffic at Intersections
- Slide 18
- Real World Results Leading Vehicles & Partial
Occlusions
- Slide 19
- Real World Results Challenges and Limits
- Slide 20
- Joint Histogram Based Cost Aggregation for Stereo Matching -
TPAMI 2013 20 Conclusion
- Slide 21
- Joint Histogram Based Cost Aggregation for Stereo Matching -
TPAMI 2013 21 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 objects 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)
- Slide 22
- 22 Thank you!