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Joint Human Detection from On-Board and Off-Board Cameras

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Joint Human Detection from On-Board and Off-Board Cameras

  1. 1. Joint Human Detection from On-Board and Off-Board Cameras Justinas Mišeikis and Paulo V. K. Borges
  2. 2. Problem Various vehicles and people share the same environment. Unfortunately, in these conditions accidents occur.
  3. 3. Problem Can we use technology to prevent them?
  4. 4. Related Work • Pedestrian detection using vehicle mounted stereo cameras • Crowd tracking in enclosed environments • People tracking using fixed cameras and driver warning if a person is on the path • Same object identification using off-board cameras and not localized mobile camera
  5. 5. Our Method Overview Off-Board camera In world coordinates Pos relative to On-Board camera the vehicle detection
  6. 6. Our Method Overview
  7. 7. Off-Board Cameras • Fixed cameras • MOG2 background segmentation • Noise filtering • Blob detection • Size and dimensions filtering • Feet position - bottom centre point of the blob
  8. 8. People Detector - HOG • Popular method for people detection • Works well in cluttered environments • Based on distribution of intensity gradients or edge directions. • Descriptor created from many samples
  9. 9. On-Board Cameras Full Image Analysis The camera view is split according A: Whole Image analysis - person not to the distance from the camera: detected blue (3-7 meters), green (7-12 B: Area split method - person detected meters), yellow (12-20 meters).
  10. 10. Targeted Analysis Expected feet Detected position from person ground plane homography HOG search area Size calculated using the expected person height
  11. 11. Data Fusion Off-Board Cam A Off-Board Cam B Detected On-Board Cam - HOG people positions Position Fusion Final Pedestrian Position
  12. 12. Data Fusion Position sensor fusion 6 HOG detector variance Estimated distance error (meters) Measurement Error 5 Polynomial Error Estimation estimation depending 4 on the object’s distance 3 2 from the camera 1 0 2 3 4 5 6 7 8 9 10 11 12 Distance from the camera (meters) Extended Kalman Filter for sensor fusion and tracking
  13. 13. Implementation • C++ • Standard Intel laptop + desktop running in parallel • cvBlob library
  14. 14. Experiments 30m by 30m industrial site Test vehicle - HMC 2 Off-Board, 1 On-Board cam Three 4-6 minute runs 1-2 People walking around Vehicle static and moving
  15. 15. Results FP % FN % Localized Analysis 4.41 3.66 Full Image Analysis 95.53 2.40 • Works real-time • Background Segmentation - 10-15 FPS • HOG detectors - 5 FPS
  16. 16. Progress • Additional front facing cameras, 4 in total - Two side cameras - Wide angle camera for close proximity - Far proximity front camera • People tracking • HOG performed on GPU GeForce GT640
  17. 17. Progress Close Prox Cam Danger Zone - reduced to around 20 cm from the front of the vehicle Far Prox Cam
  18. 18. Thank You! Any Questions?

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