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MOVING OBJECT DETECTION ON A RUNWAY PRIOR TO LANDING USING AN ONBOARD INFRARED CAMERA. Dr. Gerard Medioni Cheng Hua Pai Yu Ping Lin. Introduction. Input: Infrared runway sequence Goal: Detect moving objects on runway. Approach. We do it in two steps: Stabilize the sequence
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MOVING OBJECT DETECTION ON A RUNWAY PRIOR TO LANDING USING AN ONBOARD INFRARED CAMERA Dr. Gerard Medioni Cheng Hua Pai Yu Ping Lin
Introduction • Input: Infrared runway sequence • Goal: Detect moving objects on runway
Approach • We do it in two steps: • Stabilize the sequence • Detect motion on the stabilized sequence
Flow chart of the system Video In Reference Frame Runway Identification Image Stabilization Update Reference frame Motion Detection Yes Locally Stabilized Image Sequence and Homographies Update Reference frame? Blobs in motion
Stabilization • Issues: • Planar region containing the runway • Feature choice and matching • Transformation between consecutive frames
Stabilization • Approach • Manually Label planar region • SIFT provides sufficient and descriptive features • RANSAC to estimate best transformation
Stabilization • Result: Stabilized runway sequence
Adaptive Reference Frame • Issues: • For longer sequence • Small errors accumulate • Big scale difference Beginning of a Sequence End of a Sequence
Adaptive Reference Frame • When to change reference frame? • Check the lower edge length ratio
Stabilization algorithm Landing UAV image sequence Manually labeled planar region input Use RANSAC to remove outliers and estimate homography Extract SIFT features Region of Interest Update reference frame if necessary Match features to previous frame to establish correspondence Warp to the reference frame output Locally stabilized image sequence and for all s
Adaptive Reference Frame • Result: Original Sequence Locally Stabilized Sequence
Detection module • Issues: • Detection method • Global intensity variation • Noise • Moire in the sequence • Poor stabilization • Local intensity variation • Random noise
Detection • Approach: • Use simple Gaussian background model t = (1-) * (t-1) + * (It) t2 = (1-) * (t-1) 2 + * (It- t) • Foreground: More than 4t2 from mean Foreground Background 4t2 4t2 t Intensity distribution of an image
Global intensity variation • Approach: • Compensate gain with affine transformation [Yalcin 05] Before compensation After compensation
Noise reduction • Approach: • Moire in the sequence • Compare 8 neighbouring background pixels • Poor stabilization • Restabilize with gradient map (also SIFT) To Gradient
Noise reduction • Approach: • Local intensity variation • Intensity normalization on the foreground pixels • Random noise • Compare consecutive foreground masks With random noise Without random noise
Detection Result • Result: Foreground mask Locally Stabilized Sequence
Evaluation • Tested on 150 synthesized and 18 real-world sequences • Results (synthetic data): Obj. size
Conclusion • Detection affected by: • Object speed and size • Threshold parameters • Program limitation: • Moving objects fade in and out • Bad result near the end of the sequence • Future work: • More test on larger dataset • Speed improvement
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