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Object Speed Measurements Using Motion Blurred Images

Object Speed Measurements Using Motion Blurred Images. 林惠勇 中正大學電機系 lin@ee.ccu.edu.tw. Images …. Defocus blur:. Motion blur:. Blur Images …. Motion of Object. Region of Interest:. What Do They Tell Us?. From the movie: “Chicken Run”. Information from Blur Images.

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Object Speed Measurements Using Motion Blurred Images

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  1. Object Speed Measurements Using Motion Blurred Images 林惠勇 中正大學電機系 lin@ee.ccu.edu.tw

  2. Images… CCU Vision Lab

  3. Defocus blur: Motion blur: Blur Images… CCU Vision Lab

  4. Motion of Object Region of Interest: What Do They Tell Us? CCU Vision Lab

  5. From the movie: “Chicken Run” Information from Blur Images • Two types of image blur: • Defocus blur – due to the limitation of optical sensors • Image restoration • Identification of region of interest • Depth measurement • Motion blur – due to the relative motion between the camera and the scene • Image restoration • Motion analysis • Increase still resolution from video • Special effect • Speed measurements? CCU Vision Lab

  6. Defocus Blur Blur circle CCU Vision Lab

  7. Motion Blur CCU Vision Lab

  8. Speed Measurements • Why measure speed? (motivation) • Wind • Experiments • Sports (baseball, tennis ball), athletes • Vehicle speed detection • How? • RADAR (Radio Detection And Ranging) • LIDAR (Laser Infrared Detection And Ranging) • GPS • Video-Based Analysis CCU Vision Lab

  9. Image-Based Speed Measurement • Key idea: • For a fixed camera exposure time: Relative motion betweenobject and static camera Motion blur appeared in the dynamic image region CCU Vision Lab

  10. Geometric Formulation • Simple pinhole camera model: • Key components: • Focal length, exposure time, CCD pixel size • Object distance, blur length (blur extent) CCU Vision Lab

  11. Image Degradation • Image degradation – linear space invariant system • Characterized by its point spread function (PSF) h(x,y) • Degradation under uniform linear motion (whole image) • How about space variant case? (partial blur & total blur) CCU Vision Lab

  12. Blur Parameter Estimation • Edge detection ABC: • Sharp edge  step response • Blur edge  ramp response • How to use this fact to estimate blur extent? CCU Vision Lab

  13. g(x,y) Degradation function H Restoration filter(s) f(x,y) f(x,y) + Noise (x,y) Restoration Degradation Image Deblurring • If H is linear, space invariant: • Inverse filtering • Wiener filter • Bad news: • Our case is space variant • Region segmentation CCU Vision Lab

  14. More General Case – I • What if the object is not moving parallel to the image scanlines? • Motion direction estimation • Image rectification CCU Vision Lab

  15. Motion Direction Estimation • Fourier spectrum analysis: • It can also be implemented in spatial domain CCU Vision Lab

  16. More General Case – II • What if the object is not moving parallel to the image plane? CCU Vision Lab

  17. Extended Camera Model CCU Vision Lab

  18. Required Parameters • Intrinsic camera parameters • Focal length, CCD pixel size, exposure time • Extrinsic camera parameters • Distance to the object, camera orientation • Softball speed measurement • Size of the softball (physical measurement) • Vehicle speed detection – “parallel case” • Length of the vehicle (from manufacturer’s data sheet) • Vehicle speed detection – “non-parallel case” ? • How to obtain the parameters z, , etc.? CCU Vision Lab

  19. Vehicle Speed Detection • Parameters: • K = 22 pixels, sx = 11 m,f = 10 mm, T = 1/160 sec. • l = 560 pixels, L = 4750 m • Detected speed – 104.86 km/hr • Video-based speed – 106.11 km/hr, speed limit – 110 km/hr CCU Vision Lab

  20. Camera Pose Estimation • Theorem: • Given a parallelogram in 3-D space with known image projection of four points, their relative depths can be determined. • To obtain the unknown scale factor: • Absolute metric between two 3-D points • License plate with standard size CCU Vision Lab

  21. Vehicle Speed Detection • Parameters: • K = 22 pixels, sx = 6.8 m,T = 1/400 sec., l = 560 pixels, L = 4750 m • W = 320 mm, = 48.25, f = 26 mm • Detected speed – 112.97 km/hr • Video-based speed – 110.22 km/hr CCU Vision Lab

  22. Fully Automated? How? • Intrinsic camera parameters? • JPEG EXIF header • Target identification • Motion blur analysis • Region segmentation • Region growing • Additional image capture • Robust blur extent estimation • Image synthesis • Deblurred target region + static background region CCU Vision Lab

  23. Initial Target Segmentation • Horizontal ramp edge detection • Run-length coding or projection • Vertical continuity checking • Multiple direction analysis CCU Vision Lab

  24. Spherical Object in Motion • Problems on parameter estimation • Accuracy, robustness, precision (subpixel resolution…) • Spherical object  circular from any viewpoint • Initial blur extent identification + circle detection • Circle fitting, Hough transform • More problems • Motion blur due to rotation, three-dimensional translation, shading, etc. CCU Vision Lab

  25. Speed Measurement Flowchart CCU Vision Lab

  26. Motion Direction Estimation • Camera pose estimation – non-parallel case • Two or more captures with fast shutter speed • Vertical projection • Post-processing • Fixed object size • Could be blurred CCU Vision Lab

  27. Softball Speed Measurement • Parameters: • K = 26 pixels, T = 1/320 sec., l = 72 pixels, d = 97.45 mm • Detected speed – 40.5 km/hr • Video-based speed – 40.9 km/hr CCU Vision Lab

  28. Conclusion • Object speed measurement using a single motion blurred image • Vehicle speed detection • Softball speed measurement • Advantages • Low cost – off-the-shelf digital camera • Passive device – can avoid anti-detection • Passive device – no radiation, light • Large measurement range – through adjustable shutter speed • Limitation • Lighting condition • Accuracy? CCU Vision Lab

  29. Thank you for your attention! Any questions? CCU Vision Lab

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