automation of pedestrian tracking in a crowded situation using angled uncalibrated camera

8/14/2019 AUTOMATION OF PEDESTRIAN TRACKING IN A CROWDED SITUATION USING ANGLED UNCALIBRATED CAMERA

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AUTOMATION OFAUTOMATION OFPEDESTRIAN TRACKINGPEDESTRIAN TRACKING

IN A CROWDEDIN A CROWDED

SITUATION USINGSITUATION USINGANGLED UNCALIBRATEDANGLED UNCALIBRATED

CAMERACAMERASaman SaadatSovannarith HengKardi Teknomo

Ateneo de Manila University


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Overview

Introduction

Problem

MethodResults

Conclusion


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IntroductionThe importance of measurement of

individual pedestrian trajectories

E.g. In Business, Evacuation, and Traffic

etc.

Pedestrians movement influencing safetyconcerns in crowded locations.

E.g. such as sports venues, public transportstation, and religious events etc


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Kelly (2007) states that most ofthe person detection techniqueworks better in well controlled

environments such as laboratorysetting with a small amount ofpeople.

Literature


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Kelly, OConnor, and Smeaton (2009)shows that other techniques require morethan one camera with overlapping fields ofview in order to cope with the pedestrian

segmentation problem in the a densecrowd.

We need to use extra cameras which cost

much.

Literature

R


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How can we track all pedestrians in the sceneusing a single camera and save the data oftracking as a NTXY table?

Fig. 1 Walking experiment at Ateneo de Manila

Researc

Problem


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Method in

Automation


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Object Detection1. Split the video sequence into three channels of

Red, Green and Blue (RGB)

2. Subtracting green channel from the redchannel

3. Threshold of 70 (out of 255)

i(x, y) = r(x, y) g(x, y)

>

=

otherwise0

),(if1),(

yxiyxf

Obj D i


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Object Detection(cont.)

Obj t D t ti


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Object Detection(cont.)


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Run the particle analysis in ImageJ to:

Detect all objects in a frame by usingconnected component algorithm

Analyze each detected object for their area

and centroid coordinate

The result will be a table with ID, coordinateand slices number of each object.

Object Analysis

Obj t A l i


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Fig. 4 Results of particlesanalysis of the binary image

Fig. 5 Tabulated result of particleanalysis

Object Analysis(cont.)

Obj t A l i i


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Object Analysis inImageJ


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Flowchart of Tracking

Algorithm


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Object Tracking

Searching for the neighborhood

Obj t T ki


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Object Tracking

(cont.)


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Comparison with ManualTracking


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Research Question:How can we track all pedestrians in thescene using a single camera and savethe data of tracking as a NTXY table?

1. Red hat detection2. Blob identification

3. Frame by frame tracking of

trajectories4. Conversion to NTXY

Summary


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We were able to create algorithms to detect,to analyze and to track pedestrian fromuncalibrated angle camera for outdoorcrowded situation

The system was able to recognize

pedestrians at reasonable rate ofmisclassification

86.7% correctly detected (red hat)

13.3% maximum error (any other red

object) Accuracy can further be improved by taking

into account the size and shape of the blob asit moves (distinguishing red hat from other

red objects)

Conclusion


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Thank you

automation of pedestrian tracking in a crowded situation using angled uncalibrated camera

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