1 / 26

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

viola
Download Presentation

CS55 Tianfan Xue 2005011371 Adviser: Bo Zhang, Jianmin Li

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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)

More Related