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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
Our Method Overview Off-Board camera In world coordinates Pos relative to On-Board camera the vehicle detection
Off-Board Cameras• Fixed cameras• MOG2 background segmentation• Noise ﬁltering• Blob detection• Size and dimensions ﬁltering• Feet position - bottom centre point of the blob
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
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).
Targeted AnalysisExpected feet Detectedposition from personground plane homography HOG search area Size calculated using the expected person height
Data Fusion Off-Board Cam A Off-Board Cam BDetected On-Board Cam - HOG peoplepositions Position Fusion Final Pedestrian Position
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
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 1-2 People walking around Vehicle static and moving