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CS55 Tianfan Xue 2005011371 Adviser: Bo Zhang, Jianmin Li

CS55 Tianfan Xue 2005011371 Adviser: Bo Zhang, Jianmin Li. Outline. Introduction Original Algorithm Improved Algorithm System Design & Data Set Performance Evaluation Work Next Step. Introduction. Automatically Video Surveillance Human Tracking What is human tracking

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CS55 Tianfan Xue 2005011371 Adviser: Bo Zhang, Jianmin Li

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  1. CS55 Tianfan Xue 2005011371 Adviser: Bo Zhang, Jianmin Li

  2. Outline Introduction Original Algorithm Improved Algorithm System Design & Data Set Performance Evaluation Work Next Step

  3. Introduction • Automatically Video Surveillance • Human Tracking • What is human tracking • Why do human tracking • Presumption • Person is standing & Normal Pose

  4. Original Algorithm • Algorithm Design • General Framework • Probability Evaluation • HOG feature • Initial Detect • Motion Prediction • Drawback

  5. Original Algorithm Frame n Training Set State n-1 State n Human Detector (HOG) Predicted State n Motion prediction & Gauss Diffusion HOG features validation Position & Size Online Offline Machine learning General Framework

  6. Original Algorithm Simplified in Particle Filter Gauss Model + Motion Predict HOG output • Probability Evaluation • Definition xt : State in time t zt : Image in time t Zt : Whole image sequence till time t • Probability:

  7. Original Algorithm SVM original Edge map HOG • Initial Detect • Randomly Choose 2000 positions in an image • Motion Prediction • Linear Regression of recent 10 frame • Offline Detector • HOG features

  8. Original Algorithm • Drawbacks • Fail to find a person at emergence Detection Rate ↔ Computational Complexity • Loss track when partially Occlusion • 2-Magnet Effect

  9. Original Algorithm • Drawbacks • Fail to find a person at emergence • Loss track when partially Occlusion • 2-Magnet Effect

  10. Original Algorithm • Drawbacks • Fail to find a person at emergence • Loss track when partially Occlusion • 2-Magnet Effect When person A (more obvious) pass person B(less obvious), A will attract B’s window

  11. Improved Algorithm • 3 Improvement • Use salience to cut search space • Combine offline-online classifier(online: Color features) • Part Detector • Problems

  12. Improved Algorithm • Using Salience To Cut Search Space • Idea: The position people more like emerge (Salience) • Method: Detect at only at position with great variance

  13. Improved Algorithm State n-1 Frame n Final result Training Set Color Classifier HOG Classifier Predicted State n Color detect result Motion prediction & Gauss Diffusion Color features validation HOG features validation Size & position Online Offline Machine learning • Combine offline-online classifier(online: Color features)

  14. Improved System Color Part 27% 63% 34% 65% 7% 10% HS 20% HS 21% 32% 24% Torso Torso 49% 46% 77% 64% Leg Leg 82% 93% Whole • Part Detector (CVPR05’s, Bo Wu) 12.5% 87.5% 31% 68%

  15. Improved System Leg Color Model Torso Color Model Torso HOG Model HS Color Model HS HOG Model Visible Final Property Visible Not Visible Part Detector 2

  16. Improved System • Problems • Color model also learns the occlusion object → Always Output that all parts is visible • When a person disappear, the corresponding detect window still exists

  17. System Design Tracking System XML Debugging output GUI

  18. Data Set • Training Data • INRIA Person Data Set • 2416 Positive Examples, 1218 Negative Examples • Testing Data • PETS2004(CAVIAR)

  19. Experiment Result TP: True Positive, FP: False Positive, FN: False Negative • Evaluation • Compare ground truth windows with detected windows • Overlap:(T=0.5) • Tracker Detection Rate(TRDR) & False Alarm Rate(FAR)

  20. Experiment Result • Test2 Color Model Baseline: With Color Model, With Salience Detect Test1 Use Salience to Detect New Person

  21. Work Next Step • Improve online-offline classifier • How to learn a good color model • How to decide a person is disappeared • Make a more wide-arrange evaluation

  22. Q & A

  23. Probability Evaluation Space Too Large!!! Bayesian result Particle Filter

  24. 2-Magnet Effect Punishment for 2 close windows Gauss Model + Motion Predict HOG output • Solve 2-Magnet Effect • But it will bring some new problems…

  25. Color Model Features: 72-dim HSV histogram Probability Evaluation: Inner Product of 2 feature vectors

  26. Detect Result Performance of other algorithm (Here, different evaluation standard was used)

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