CS55 Tianfan Xue 2005011371Adviser: Bo Zhang, Jianmin Li

OutlineIntroductionOriginal AlgorithmImproved AlgorithmSystem Design & Data SetPerformance EvaluationWork Next Step

IntroductionAutomatically Video SurveillanceHuman Tracking

What is human trackingWhy do human tracking

PresumptionPerson is standing & Normal

Pose

Original AlgorithmAlgorithm Design

General FrameworkProbability EvaluationHOG featureInitial DetectMotion Prediction

Drawback

Original AlgorithmGeneral Framework

Frame n

State n-1Predicted State n

HumanDetector(HOG)

State n

Motion prediction & Gauss Diffusion

Position & Size

HOG features validation

Training Set Machine learning

Offline

Online

Original AlgorithmProbability Evaluation

Definitionxt : State in time t

zt : Image in time t Zt : Whole image sequence till time t

Probability:1 1 1 1( | ) ( | ) ( | ) ( | )t t t t t t t t tp x Z p z x p x x p x Z dx

1( | ) ( | ) ( | )i i i i it t t t t tx Z p x x p z x

Gauss Model + Motion Predict

HOG output

Simplified in Particle Filter

1

Ni i

t ti

x x

Original AlgorithmInitial Detect

Randomly Choose 2000 positions in an imageMotion Prediction

Linear Regression of recent 10 frameOffline Detector

HOG features

original Edge map

HOG

SVM

Original AlgorithmDrawbacks

Fail to find a person at emergence Detection Rate ↔

Computational ComplexityLoss track when partially Occlusion2-Magnet Effect

Original AlgorithmDrawbacks

Fail to find a person at emergence

Loss track when partially Occlusion

2-Magnet Effect

Original AlgorithmDrawbacks

Fail to find a person at emergence

Loss track when partially Occlusion

2-Magnet EffectWhen person A (more obvious) pass person B(less obvious), A will attract B’s window

Improved Algorithm3 Improvement

Use salience to cut search spaceCombine offline-online classifier(online: Color features)Part Detector

Problems

Improved AlgorithmUsing Salience To Cut

Search SpaceIdea:

The position people more like emerge (Salience)

Method:Detect at only at position with great variance

Improved AlgorithmCombine offline-online classifier(online: Color features)

Frame n

State n-1Color detect result

Predicted State n

HOGClassifier

Final result

Motion prediction & Gauss Diffusion

Size & position

Color features validation

HOG features validation

ColorClassifier

Training Set

Machine learning

Offline

Online

Improved SystemPart 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%

Improved SystemPart Detector 2

LegColor Model

Not Visible

TorsoColor Model

Visible

HSColor Model

Visible

TorsoHOG

Model

HSHOG

ModelFinal Property

Improved SystemProblems

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 DesignTracking SystemXML Debugging outputGUI

Data SetTraining Data

INRIA Person Data Set2416 Positive Examples, 1218 Negative Examples

Testing DataPETS2004(CAVIAR)

Experiment ResultEvaluation

Compare ground truth windows with detected windowsOverlap:(T=0.5)

Tracker Detection Rate(TRDR) & False Alarm Rate(FAR)

| |2*| | | |

obs truth

obs truth

A AT

A A

TPTRDR

TP FN

FP

FARTP FP

TP: True Positive, FP: False Positive, FN: False Negative

Experiment ResultBaseline: With Color Model, With Salience DetectTest1 Use Salience to Detect New Person

Random Select Detect

Pos

Select At Salience

Time 15.9s/frame 4.5s/frame

TRDR 61.1% 66.8%

FAR 21.9% 15.6%Test2 Color ModelWithout Color

ModelWith Color

Model

Time 2.2s/frame 4.5s/frame

TRDR 9.8% 66.8%

FAR 20.4% 15.6%

Work Next StepImprove online-offline classifier

How to learn a good color modelHow to decide a person is disappeared

Make a more wide-arrange evaluation

Q & A

Probability EvaluationBayesian result

Particle Filter1 1 1 1( | ) ( | ) ( | ) ( | )t t t t t t t t tp x Z p z x p x x p x Z dx

1( | ) ( | ) ( | )t t t t t tx Z p z x p x x Space Too Large!!!

2-Magnet EffectSolve 2-Magnet Effect

But it will bring some new problems…

1( | ) ( | ) ( | ) ( )t i t t t t overlapx Z p x x p z x p x

Gauss Model + Motion Predict

HOG output Punishment for 2 close windows

No ColorNo overlap

term

No ColorOverlap term

ColorNo overlap

term

Coloroverlap term

TRDR 46.9% 9.8% 66.8% 9.8%

FAR 42.1% 20.4% 15.6% 20.0%

Color ModelFeatures:

72-dim HSV histogramProbability Evaluation:

Inner Product of 2 feature vectors

Detect ResultPerformance of other algorithm (Here, different

evaluation standard was used)TRDR FAR

Our Method 56.1% 29.4%

BBS 42.5% 72.4%

W4 11.7% 92.1%

SGM 42.8% 54.0%

MGM 38.2% 63.3%

LOTS 47.9% 40.3%

Track 44.4% 35.2%