Multi Object Tracking | Presentation 1 | ID 103001
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Slide for Multi Object Tracking by Md. Minhazul Haque, Rajshahi University of Engineering and Technology * Object * Object Tracking * Application * Background Study * How it works * Multi-Object Tracking * Solution * Future Works
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Multi Object Tracking | Presentation 1 | ID 103001
- 1. title Multi-Object Tracking using Computer Vision Heaven's Light is Our Guide Rajshahi University of Engineering and Technology Department of Computer Science and Engineering Presented by Md. Minhazul Haque Roll # 103001 Dept. of CSE RUET Supervised by Md. Arafat Hossain Assistant Professor Dept. of CSE RUET August 04, 2015
- 2. Table of Contents ❏ Object ❏ Object Tracking ❏ Application ❏ Background Study ❏ How it works ❏ Multi-Object Tracking ❏ Solution ❏ Future Works 2/23 Multi-Object Tracking using Computer Vision August 04, 2015
- 3. The Cars August 04, 2015 Image Courtesy: Flickr Multi-Object Tracking using Computer Vision3/23
- 4. Object Object ❏ A group of pixels with similar property ❏ A blob or reign of an image Anything can be an Object ❏ A ball ❏ A car ❏ A bird ❏ Even you! August 04, 2015Multi-Object Tracking using Computer Vision4/23
- 5. Objects (cont.) August 04, 2015 A bird A car A human Image Courtesy: 4freephotos Multi-Object Tracking using Computer Vision5/23
- 6. Object Tracking ❏ Locate Objects over time ❏ Save Object List into memory ❏ Set unique ID to each Object ❏ Loop until media/input ends August 04, 2015Multi-Object Tracking using Computer Vision6/23
- 7. Applications of Object Tracking Object Tracking could be helpful regarding - ❏ Apply Security Policies ❏ Biomedical Research ❏ Vehicle Routing ❏ Drone Controls ❏ Smart Car August 04, 2015 Image Courtesy: ImImg, SchoolOfMotoring Multi-Object Tracking using Computer Vision7/23
- 8. Background Study ❏ Contour-Based Object Tracking with Occlusion Handling - Alper Yilmaz, Xin Li, Mubarak Shah, IEEE ❏ Fast and Automatic Video Object Segmentation and Tracking - Changick Kim and Jenq-Neng Hwang ❏ Kernel-Based Object Tracking - Dorin Comaniciu, Visvanathan Ramesh, Peter Meer, IEEE ❏ Object Tracking Using CamShift Algorithm and Multiple Quantized Feature Spaces - John G. Allen, Richard Y. D. Xu, Jesse S. Jin, University of Sydney August 04, 2015Multi-Object Tracking using Computer Vision8/23
- 9. ∞ How Object Tracking Works Tracking = (Detection + Recognition + Processing) August 04, 2015 Image Courtesy: Shakthydoss, Dodlive, Virus-IT Multi-Object Tracking using Computer Vision9/23
- 10. Steps of Object Tracking August 04, 2015 Start Initialize source media Apply BGS Apply Contour Detection Get Object List Track Objects Update Objects Delete Objects Add Objects Stream of frames Get a frame Loop until end of media/frame Multi-Object Tracking using Computer Vision10/23
- 11. Object Tracking Methods ❏ CamShift Constantly Adaptive Mean Shift, Histogram based Tracker ❏ Kalman Filter Linear Quadratic Estimation developed by Rudolf E. Kálmán ❏ Particle Filter Monte Carlo method based on probability densities August 04, 2015Multi-Object Tracking using Computer Vision11/23
- 12. CamShift ❏ Known as Constantly Adaptive MeanShift ❏ Calculates Shift Vector of Object ❏ Saves Object Histogram into memory ❏ Looks for Object in all possible directions from current position ❏ Search area is expanded if Object not found August 04, 2015Multi-Object Tracking using Computer Vision12/23
- 13. CamShift (cont.) August 04, 2015 Position 1 All OK Position 2 Found inside search area Position 3 Search area expanded Search area❏ How CamShift works Multi-Object Tracking using Computer Vision13/23
- 14. CamShift (cont.) Pros ❏ Tracks object faster ❏ Easy to implement Cons ❏ Color based motion tracker ❏ Loses track easily when similar colored objects are nearby August 04, 2015 Image Courtesy: Cliparthunt, Vectors4all Found a track! We are lost! Multi-Object Tracking using Computer Vision14/23
- 15. Kalman Filter ❏ Kernel based estimation algorithm ❏ Uses 3 position matrix 1. Previous Position 2. Current Position 3. Predicted Position ❏ Updates all of them continuously Predicted = K × Current + (K-1) × Previous K = Kalman Filter Gain August 04, 2015Multi-Object Tracking using Computer Vision15/23
- 16. Kalman Filter (cont.) August 04, 2015 Position 1 All OK Position 2 Calculate Gain K Position 3 Predict new position ❏ How Kalman Filter works Gain at K-1 Predicted Gain at K Update Gain as slightly mismatched Multi-Object Tracking using Computer Vision16/23
- 17. Kalman Filter (cont.) Pros ❏ Mathematically precise ❏ Tracks rouge objects ❏ Removes noise from data Cons ❏ Complex to implement ❏ Position based estimator algorithm August 04, 2015 Image Courtesy: PMacStrong Multi-Object Tracking using Computer Vision17/23
- 18. Multi-Object Tracking August 04, 2015 Why do we need Multi-Object Tracking? ❏ Real world has more Objects to track at a time (i.e. a highway) ❏ CamShift or Kalman Filter cannot handle Multi-Object Tracking alone ❏ Noise and unwanted Object makes tracking more challenging ❏ A new system needs to be implemented Multi-Object Tracking using Computer Vision18/23
- 19. Multi-Object Tracking (cont.) August 04, 2015 Photo taken at RUET CampusExpected Multi-Object Tracking System Multi-Object Tracking using Computer Vision19/23
- 20. ❏ More feature extraction ❏ Motion ❏ Object Size ❏ Object Orientation ❏ Add fallback tracking algorithm ❏ Better Background Subtraction ❏ Occlusion Handling Solution? August 04, 2015 Image Courtesy: RockyTopSportsWorld Multi-Object Tracking using Computer Vision20/23
- 21. ❏ Collect datasets (videos of highway, campus area etc.) ❏ Implement new model for Multi-Object tracking ❏ Compare BGS models ❏ Create a GUI for easy handling Future Works August 04, 2015Multi-Object Tracking using Computer Vision21/23
- 22. Any questions? Further contact Mail to minhaz@linux.com Visit https://minhazulhaque.com