Joint Human Detection from On-Board        and Off-Board Cameras       Justinas Mišeikis and Paulo V. K. Borges
ProblemVarious vehicles and people share the same environment.   Unfortunately, in these conditions accidents occur.
ProblemCan we use technology to     prevent them?
Related Work• Pedestrian detection using vehicle mounted  stereo cameras• Crowd tracking in enclosed environments• People ...
Our Method Overview   Off-Board camera   In world coordinates    Pos relative to   On-Board camera     the vehicle        ...
Our Method Overview
Off-Board Cameras• Fixed cameras• MOG2 background segmentation• Noise filtering• Blob detection• Size and dimensions filteri...
People Detector - HOG• Popular method for people detection• Works well in cluttered environments• Based on distribution of...
On-Board Cameras           Full Image AnalysisThe camera view is split according    A: Whole Image analysis - person not t...
Targeted AnalysisExpected feet                                Detectedposition from                                 person...
Data Fusion    Off-Board Cam A              Off-Board Cam BDetected      On-Board Cam - HOG peoplepositions               ...
Data Fusion    Position sensor fusion                                                                   6    HOG detector ...
Implementation• C++• Standard Intel laptop + desktop running in  parallel• cvBlob library
Experiments    30m by 30m industrial site    Test vehicle - HMC    2 Off-Board, 1 On-Board cam    Three 4-6 minute runs   ...
Results                        FP %     FN %  Localized Analysis     4.41     3.66  Full Image Analysis   95.53     2.40• ...
Progress• Additional front facing cameras, 4 in total - Two side cameras - Wide angle camera for close proximity - Far pro...
Progress                                  Close Prox Cam  Danger Zone -reduced to around  20 cm from thefront of the vehic...
Thank You!Any Questions?
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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. ProblemVarious vehicles and people share the same environment. Unfortunately, in these conditions accidents occur.
    3. 3. ProblemCan 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 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. 10. Targeted AnalysisExpected feet Detectedposition from personground plane homography HOG search area Size calculated using the expected person height
    11. 11. Data Fusion Off-Board Cam A Off-Board Cam BDetected On-Board Cam - HOG peoplepositions 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 thefront of the vehicle Far Prox Cam
    18. 18. Thank You!Any Questions?

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