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Published byDarren Stokes Modified over 9 years ago
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CS55 Tianfan Xue 2005011371 Adviser: Bo Zhang, Jianmin Li
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Outline Introduction Original Algorithm Improved Algorithm System Design & Data Set Performance Evaluation Work Next Step
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Introduction Automatically Video Surveillance Human Tracking What is human tracking Why do human tracking Presumption Person is standing & Normal Pose
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Original Algorithm Algorithm Design General Framework Probability Evaluation HOG feature Initial Detect Motion Prediction Drawback
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Original Algorithm General Framework Frame n State n-1 Predicted State n Human Detector (HOG) State n Motion prediction & Gauss Diffusion Position & Size HOG features validation Training Set Machine learning Offline Online
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Original Algorithm Probability Evaluation Definition x t : State in time t z t : Image in time t Z t : Whole image sequence till time t Probability: Gauss Model + Motion Predict HOG output Simplified in Particle Filter
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Original Algorithm Initial Detect Randomly Choose 2000 positions in an image Motion Prediction Linear Regression of recent 10 frame Offline Detector HOG features original Edge mapHOG SVM
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Original Algorithm Drawbacks Fail to find a person at emergence Detection Rate ↔ Computational Complexity Loss track when partially Occlusion 2-Magnet Effect
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Original Algorithm Drawbacks Fail to find a person at emergence Loss track when partially Occlusion 2-Magnet Effect
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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
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Improved Algorithm 3 Improvement Use salience to cut search space Combine offline-online classifier(online: Color features) Part Detector Problems
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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
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Improved Algorithm Combine offline-online classifier(online: Color features) Frame n State n-1 Color detect result Predicted State n HOG Classifier Final result Motion prediction & Gauss Diffusion Size & position Color features validation HOG features validation Color Classifier Training Set Machine learning Offline Online
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Improved System Part Detector (CVPR05’s, Bo Wu) 7% 32% 49% 93% 20% 64% 10% 24% 46% 82% 21% 77% 12.5%87.5% 34%65% 31%68% HS Torso Leg HS Torso Leg Color Part Whole 27%63%
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Improved System Part Detector 2 Leg Color Model Not Visible Torso Color Model Visible HS Color Model Visible Torso HOG Model HS HOG Model Final Property
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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
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System Design Tracking System XML Debugging output GUI
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Data Set Training Data INRIA Person Data Set 2416 Positive Examples, 1218 Negative Examples Testing Data PETS2004(CAVIAR)
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Experiment Result Evaluation Compare ground truth windows with detected windows Overlap:(T=0.5) Tracker Detection Rate(TRDR) & False Alarm Rate(FAR) TP: True Positive, FP: False Positive, FN: False Negative
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Experiment Result Baseline: With Color Model, With Salience Detect Test1 Use Salience to Detect New Person Random Select Detect Pos Select At Salience Time15.9s/frame4.5s/frame TRDR61.1%66.8% FAR21.9%15.6% Test2 Color Model Without Color Model With Color Model Time2.2s/frame4.5s/frame TRDR9.8%66.8% FAR20.4%15.6%
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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
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Q & A
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Probability Evaluation Bayesian result Particle Filter Space Too Large!!!
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2-Magnet Effect Solve 2-Magnet Effect But it will bring some new problems… Gauss Model + Motion Predict HOG output Punishment for 2 close windows No Color No overlap term No Color Overlap term Color No overlap term Color overlap term TRDR46.9%9.8%66.8%9.8% FAR42.1%20.4%15.6%20.0%
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Color Model Features: 72-dim HSV histogram Probability Evaluation: Inner Product of 2 feature vectors
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Detect Result Performance of other algorithm (Here, different evaluation standard was used) TRDRFAR Our Method56.1%29.4% BBS42.5%72.4% W411.7%92.1% SGM42.8%54.0% MGM38.2%63.3% LOTS47.9%40.3% Track44.4%35.2%
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