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Kalman Tracking for Image Processing Applications

Kalman Tracking for Image Processing Applications. Student : Julius Oyeleke Supervisor : Dr Martin Glavin Co-Supervisor : Dr Fearghal Morgan. Objective of Project. To track a red ball over a frame of video Image Thresholding 3 . Find the centre point of the ball

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Kalman Tracking for Image Processing Applications

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  1. Kalman Tracking for Image Processing Applications Student:Julius Oyeleke Supervisor: Dr Martin Glavin Co-Supervisor: Dr Fearghal Morgan

  2. Objective of Project • To track a red ball over a frame of video • Image Thresholding 3. Find the centre point of the ball 4. The use of Kalman filtering to • track the red ball in the image. • predict the path of the ball in future as an aid of detection. 5.Display with Overlay • OpenCV (computer vision library) is being used in this project

  3. Why OpenCV • Real time computer vision. • Provides powerful function to assist in object identification, motion tracking etc. • Virtually assist in any image processing application. • C-based program computer vision repository.

  4. Step1:Image Acquiring • commission the OpenCV system to load frames of video into memory.

  5. Step1: Problem & Solution Problem: • Commissioning OpenCV to read images • Installation of OpenCV 2.0 Solution: • Uninstall OpenCV 2.0 • Install OpenCV 1.0

  6. Step2: Image Thresholding • convert the RGB frames to the HSV format. RGB HSV RGB HSV RGB • threshold the HSV to identify the region of interest. RGB HSV Threshold RGB output to screen //Create gray image

  7. Step2: Problems & Solutions Problems: • Circle Detection with OpenCV 1.0 • OpenCV 1.0 takes hue value to be 0-255 Solutions: • Uninstall OpenCV 1.0 • Install OpenCV 2.0 • In OpenCV 2.0 hue value is 0-180(works better for the red colour detection) • OpenCV 2.0 has a better algorithm for circle detection.

  8. C-make • C-make helped in compiling OpenCV from the source code • OpenCV 2.0 needs different files for different versions of studio. • One will need to complete visual studio 2008 for OpenCV 2.0

  9. Example 1:

  10. Example2

  11. Step3: Centre Point detection • Finding the centre point of the red ball • Hough transform

  12. Step4: Implementation of the Kalman Filtering Kalman Tracking-Predicting the path of the Red ball

  13. Centre point& predicted values

  14. Step4: Problems & Solutions Problems: • Kalman not tracking & predicting properly • OpenCV only has a 1-D example • Program Crashed at the line CvKalmanCorrect( Kalman, z_k );// Correct Kalman filter state Solutions: • 2-D was needed for this project • I added "if (circles->total > 0)

  15. Step5: Display with Overlay Displaying with overlay

  16. Conclusions • Project was hampered by issues, most of which were overcome. • Ambitious goal of the project was fully fulfilled • Further work would lead to a complete solution

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