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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
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 • Why do human tracking • Presumption • Person is standing & Normal Pose
Original Algorithm • Algorithm Design • General Framework • Probability Evaluation • HOG feature • Initial Detect • Motion Prediction • Drawback
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
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:
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
Original Algorithm • Drawbacks • Fail to find a person at emergence Detection Rate ↔ Computational Complexity • Loss track when partially Occlusion • 2-Magnet Effect
Original Algorithm • Drawbacks • Fail to find a person at emergence • Loss track when partially Occlusion • 2-Magnet Effect
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
Improved Algorithm • 3 Improvement • Use salience to cut search space • Combine offline-online classifier(online: Color features) • Part Detector • Problems
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
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)
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%
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
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
System Design Tracking System XML Debugging output GUI
Data Set • Training Data • INRIA Person Data Set • 2416 Positive Examples, 1218 Negative Examples • Testing Data • PETS2004(CAVIAR)
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)
Experiment Result • Test2 Color Model Baseline: With Color Model, With Salience Detect Test1 Use Salience to Detect New Person
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
Probability Evaluation Space Too Large!!! Bayesian result Particle Filter
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…
Color Model Features: 72-dim HSV histogram Probability Evaluation: Inner Product of 2 feature vectors
Detect Result Performance of other algorithm (Here, different evaluation standard was used)