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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

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  • Transcript

    • 1. Joint Human Detection from On-Board and Off-Board Cameras Justinas Mišeikis and Paulo V. K. Borges
    • 2. ProblemVarious vehicles and people share the same environment. Unfortunately, in these conditions accidents occur.
    • 3. ProblemCan we use technology to prevent them?
    • 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. Our Method Overview Off-Board camera In world coordinates Pos relative to On-Board camera the vehicle detection
    • 6. Our Method Overview
    • 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. 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. On-Board Cameras Full Image AnalysisThe 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. Targeted AnalysisExpected feet Detectedposition from personground plane homography HOG search area Size calculated using the expected person height
    • 11. Data Fusion Off-Board Cam A Off-Board Cam BDetected On-Board Cam - HOG peoplepositions Position Fusion Final Pedestrian Position
    • 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. Implementation• C++• Standard Intel laptop + desktop running in parallel• cvBlob library
    • 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. 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. 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. Progress Close Prox Cam Danger Zone -reduced to around 20 cm from thefront of the vehicle Far Prox Cam
    • 18. Thank You!Any Questions?

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