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Event Detection Using an Attention-Based Tracker Event Detection Using an Attention-Based Tracker

Event Detection Using an Attention-Based Tracker - PowerPoint Presentation

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Event Detection Using an Attention-Based Tracker - PPT Presentation

22 Outubro 2007 Universidade Federal do Paraná Gerald Dalley Xiaogang Wang and W Eric L Grimson Glasgow Airport 4 cameras 9 video clips 1 for training 8 for testing Dataset Description ID: 488923

tracking background luggage detection background tracking detection luggage blobs blob event subtraction illumination late amp normalization modeling model biased

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Slide1

Event Detection Using an Attention-Based Tracker

22

Outubro 2007. Universidade Federal do Paraná.Gerald Dalley, Xiaogang Wang, and W. Eric L. GrimsonSlide2

Glasgow Airport4 cameras9 video clips1 for training8 for testingDataset Description

2Slide3

LoiteringStaying in the scene for > 1 minuteTheftTaking someone else’s baggageLeft luggageLeaving the scene without your luggage

EvaluationCorrect event timesCorrect 3D locationsWithout false detections

Challenge Problem3Slide4

Basic tracking firstDetect interesting tracksImprove interesting tracks to detect eventsOur Approach

4Slide5

Processing Pipeline5

Tracking & Event Detection

Input Data

Background Subtraction

Background

Modeling

Illumination

NormalizationSlide6

Illumination Changes6

BACKGROUND

126

999

2999

S08Slide7

Illumination Changes by Clip7Slide8

Background Modeling8

Tracking & Event Detection

Input Data

Background Subtraction

Robust Gaussian Fit

Ground-Plane Registration

Background Model Search

Normalized

BACKGROUND

Normalized

S01-S08

Background

Modeling

Background Models

Illumination

NormalizationSlide9

Fundamental assumptionforeground is rare at every pixelReality for PETS 2007…background is rare for the pixels we care about mostSome pixels: foreground as much as 90% of the time

Breaking Adaptive Background Subtraction

9

0

2000

3000

4000Slide10

Robust Gaussian Fit (per pixel)10BACKGROUND clipForeground is rare everywhere

Fit a GaussianRefit to inliersSlide11

Need for Model Adaptation11Another clip (S02)

BACKGROUND’s model: Suboptimal fit

BACKGROUND

’s Gaussian model

Background samples

Foreground samplesSlide12

Model Adaptation12Until convergenceFind inliersShift Gaussian centerSlide13

Improvement13AdaptationIs robustImproves FG/BG classification rates

Before adaptation

After adaptationSlide14

Background Subtraction

14

Tracking & Event Detection

Input Data

Background Subtraction

Background

Modeling

Illumination

Normalization

MRF

blobs

Blob Extraction

Mahalanobis Distance

Background Models

Normalized

S01-S08Slide15

Background Subtraction15

Frame 2000

With Foreground Mask

Foreground Mask

Mahalanobis Distances

Adapted Model

Normalized

-

=

MRFSlide16

Tracking & Event Detection

16

Tracking & Event Detection

Input Data

Background Subtraction

Background

Modeling

Illumination

Normalization

alerts

blobs

Kalman Tracking

Meanshift

Tracking

Event DetectionSlide17

Idea: Focus on tracking what we care about.Loitering humansDropped luggage that becomes dissociated from its ownerKalman trackingConstant velocityLow false positive rate

Blob Tracking

17Slide18

Loitering humansRemain in the scene for a long timeLikely to create isolated tracksDispossessed luggageLikely to create at least an isolated blob detection

Detecting Humans and Luggage

18Object Type

Min. Blob

Area (% of frame)

Max. Blob Area (% of frame)

Min. Blob Track Length

humans

1.5%

3.0%

16s

luggage

0.2%

1.0%

1 frameSlide19

Blob trackingYields high-quality tracks (good)Requires isolated blobs (bad)Meanshift trackerLearn a model (color histogram) from the good blob tracksTracks through occlusions

HumansFind scene entry/exit timesLuggage

Find drop/pickup timesAssociate with human ownersMean-Shift Tracking19Slide20

Results20Slide21

No events occurNone detectedS00 – No Defined Behavior21Slide22

Staged loitering

5.1s late

S01 – General Loitering 1 (Easy)Slide23

Staged loitering1.4s lateS02 – General Loitering 2 (Hard)

23Slide24

Staged loitering8.2s lateDropped luggageShould not trigger an alarmNo alarm triggered

Staged loitering man near purple-outlined woman

MissedUnscripted loiteringDetectedS03 – Bag Swap 1 (Easy)

24Slide25

S04 – Bag Swap 2 (Hard)25

Stay close to each other the whole timeSlide26

S05 – Theft 1 (Easy)26

Victim enters

19.2s late

Luggage stolen

0.08s late

Thief exits

0.08s lateSlide27

S06 – Theft 2 (Hard)27

Thief

Luggage (never isolated)

Victims

Assistant thiefSlide28

S07 – Left Luggage 1 (Easy)28

Luggage dropped

0.12s late

Owner tracked

Luggage taken

0.08s late

By ownerSlide29

S08 – Left Luggage 2 (Hard)29

Owner

drops

: 0.2s late

Owner

picks up

: 0.12s lateSlide30

PETS 2007Problem description, datasets, etc.http://www.pets2007.netMy WebsiteThe paperhttp://people.csail.mit.edu/dalleyg

Websites

30Slide31

Perguntas31Slide32

Illumination Normalization32

whereSlide33

Region-of-Interest Masks33

BG-S04

S06

S05

S07-S08Slide34

Background Subtraction

34

Tracking & Event Detection

Input Data

Background Subtraction

Background

Modeling

Illumination

Normalization

BG-Biased MRF

FG-Biased MRF

Blob Extraction

fragmented blobs

merged blobs

Blob Extraction

Mahalanobis Distance

Background Models

Normalized

S01-S08Slide35

Dual Background Subtraction35Low fragmentation

But blobs mergedGood for human tracking

Mahalanobis Distance Map

Background-Biased Blobs

Foreground-Biased Blobs

Sharp boundaries

But fragmented blobs

Good for dropped luggage detectionSlide36

Tracking & Event Detection

36

Tracking & Event Detection

Input Data

Background Subtraction

Background

Modeling

Illumination

Normalization

alerts

fragmented blobs

merged

blobs

Kalman Tracking

Kalman Tracking

Meanshift

Tracking

Event DetectionSlide37

Luggage: it often doesn’t travel far from the owner, so we need BG-biased to avoid merging the dropped luggage blob with the ownerThe floor is more boring (except for specularities), so camouflaging doesn’t occur much there, relative to the vertical surfacesEasy to tell people fragments from luggage: small people fragments moveThey move when they’re isolated

They move before and after isolationHumansWith the busyness of the scene, a BG-biased MRF produces a lot of fragments and many-to-many blob matching quickly becomes impractical

A FG-biased MRF avoids the fragmentation issue but merges lots of blobsWe only care about loiterersLoiterers are in the scene for a long timeThey’re likely to be isolated from other people at least at some point in timeWhy Dual Trackers/Motion Blobs

37Slide38

Oriented Ellipsoids38