Dynamic 3D Scene Analysis from a Moving Vehicle Young Ki Baik (CV Lab.) (Wed)

Dynamic Scene Analysis from a Moving Vehicle  References Dynamic 3D Scene Analysis from a Moving Vehicle Bastian Leibe, Nico Cornelis, Kurt cornelis, Luc Van Gool Awarded the best paper prize (CVPR 2007) Fast Compact City Modeling for Navigation Pre- Visualization Nico Cornelis, Kurt cornelis, Luc Van Gool (CVPR 2006) Pedestrian detection in crowded scene Bastian Leibe et. al. (CVPR 2005) Putting Objects in Perspective Derek Hoiem et. al. (CVPR 2006)

Dynamic Scene Analysis from a Moving Vehicle  Why? … were they received the best paper prize? They completed the impressive real application with only toy computer vision algorithm. They showed that the field of vision will be a key of future technique to the public.

Dynamic Scene Analysis from a Moving Vehicle  Demo (Final result)

Dynamic Scene Analysis from a Moving Vehicle  What? …is the purpose of this paper? … is the challenges of this paper? Detect object in real environment (city road) Localize them in 3D Predict their future motion We are moving Objects can be moving Ground may not be planar

Dynamic Scene Analysis from a Moving Vehicle  What methods? … are used to accomplish their purpose? Structure from motion 2D object detection 3D trajectory estimation

Dynamic Scene Analysis from a Moving Vehicle  Overall flow 3D structure info.3D structure info. Ground planeGround plane 1. SfM Stereo camera Aligned stereo image 2. Object detection 2D and 3D Object2D and 3D Object 3D trajectory3D trajectory OrientationOrientation 3. Tracking

Dynamic Scene Analysis from a Moving Vehicle  3D structure and ground plane 3D Structure from Motion Visual odometry (David Nister) Use pre-calibrated stereo camera Use rectified stereo images Parallel processing → Extrinsic camera parameters → 3D camera trajectory (in real time) Nico Cornelis et. al. (CVPR 2006)

Dynamic Scene Analysis from a Moving Vehicle  3D structure and ground plane Ground plane estimation Known ground positions of wheel base points Nico Cornelis et. al. (CVPR 2006)

Dynamic Scene Analysis from a Moving Vehicle  3D structure and ground plane Ground plane estimation Compute normal locally Average over spatial window Nico Cornelis et. al. (CVPR 2006)

Dynamic Scene Analysis from a Moving Vehicle  SfM Demo Nico Cornelis et. al. (CVPR 2006)

Dynamic Scene Analysis from a Moving Vehicle  Object detection 2D/3D Interaction method Likelihood of 3D object hypothesis H → Given image I and a set of 2D detections h:

Dynamic Scene Analysis from a Moving Vehicle  Object detection 2D object detection 2D recognition ISM detectors Leibe et. al. (CVPR 2005)

Dynamic Scene Analysis from a Moving Vehicle  Object detection ISM detectors (Leibe et al., CVPR’05, BMVC’06) Battery of 5(car)+1(human) single view detectors Each detectors based on 3 local cues Harris-Laplace, Hessian-Laplace, DoG interest regions Local Shape Context descriptors Result: detections + segmentations Leibe et. al. (CVPR 2005)

Dynamic Scene Analysis from a Moving Vehicle  Object detection 2D/3D transfer Two image-plane detections are consistent if they correspond to the same 3D object. → Cluster 3D detections → Multi-viewpoint integration

Dynamic Scene Analysis from a Moving Vehicle  Object detection 3D prior By Using 3D structure and ground plane constraint… → Distance prior (Distance from the ground plane) → Size prior (Gaussian) Hoiem et. al. (CVPR 2006) Significantly reduced search space and outlier

Dynamic Scene Analysis from a Moving Vehicle  Quantitative results of detection Detection performance on 2 test sequences Stereo and Ground plane constraints significantly improves precision

Dynamic Scene Analysis from a Moving Vehicle  Detection Demo

Dynamic Scene Analysis from a Moving Vehicle  Object tracking Localization and Trajectory estimation By using detection results Obtain orientation of objects Space-time trajectory analysis By using the concept of a bidirectional Extended Kalman Filter

Dynamic Scene Analysis from a Moving Vehicle  Object tracking 3D Localization for static objects (car) Location Mean-shift search to find set of 3D detection hypotheses Orientation Cluster shape and detector output

Dynamic Scene Analysis from a Moving Vehicle  Object tracking Dynamic model Holonomic motion (Pedestrian) Without external constraints linking its speed and turn rate Nonholonomic motion (Car) Only move along its main axis Only turn while moving

Dynamic Scene Analysis from a Moving Vehicle  Object tracking Trajectory growing Collect detection in time space

Dynamic Scene Analysis from a Moving Vehicle  Object tracking Trajectory growing Collect detection in time space Evaluate under trajectory Bi-directionally Static assumption

Dynamic Scene Analysis from a Moving Vehicle  Object tracking Trajectory growing Collect detection in time space Evaluate under trajectory Bi-directionally Static assumption Adjust trajectory Weighted mean Predicted position Supporting observations

Dynamic Scene Analysis from a Moving Vehicle  Object tracking Trajectory growing Collect detection in time space Evaluate under trajectory Bi-directionally Static assumption Adjust trajectory Weighted mean Predicted position Supporting observations Iteration

Dynamic Scene Analysis from a Moving Vehicle  Object tracking Trajectory growing Collect detection in time space Evaluate under trajectory Bi-directionally Static assumption Adjust trajectory Weighted mean Predicted position Supporting observations Iteration Location and orientation

Dynamic Scene Analysis from a Moving Vehicle  Demo (Final result)

Dynamic Scene Analysis from a Moving Vehicle  Conclusion Summary Exact value of 3D information help to propose the new concept of detection algorithm raise the performance of detection algorithm. Better detection results Give more reliable tracking results Good orientation estimation Contribution New detection algorithm using 3d information Good integration and visualization of application system