Vision-Based Technologies for Security in LogisticsINFOTECH is the Unit of ROBOTIKER-TECNALIA...

Vision-Based Technologies for Security in Logistics

Alberto Isasi

[email protected]

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INFOTECH is the Unit of ROBOTIKER-TECNALIA specialised in Research, Development and Application of Information and Communication Technologies (ICT), not only in the enterprise framework but also in the Digital Society.

INFOTECH provides solutions to thoseSectors in which ICT Technologies are the key for generation and/or exploitationof added value:

� Logistic and Industrial Sector� Tourism and Entertainment.


Technological Areas

� Machine Vision.

� Semantic Technologies.

� Biometry.

� Voice Synthesis and Recognition.

� Trazability.

� Digital Ecosystems.


Application Areas

CA1Image Processing

CA2Mobile Services

CA3Voice based Services

CA4Supply ChainManagement

Industrial Control Access Control

Infrastructure Security

Visual Content Processsing

Man-Machine Interfaces

Location-based services

Portable Device Services

Picking

Audio Content Processing

Supply Chain Scheduling

Unimodal & multimodal Scheduling

Trazability of people,machines and vehicles

Interoperability


Machine Vision Overview

� Machine Vision Overview� 3D Image Processing� Biometrics

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Practical Machine Vision

� What is involved in a Machine Vision project??



� So, machine vision is not just a matter of computer and image processing algorithms:

�Lighting and optics�Acquisition system�Mechanics�Software�Time�Robustness


� So, What is Computer Vision and Image Processing?�“Automatic or assisted process of information

acquired by cameras”�Acquisition through visual resources: Hardware Tools�Automatic process: Software

SCENELIGHTSLIGHTSOPTICSOPTICS

HARDWAREHARDWARE

IMAGE IN

MEMORYIMAGE IMAGE PROCESSINGPROCESSING

INFORMATION

RESULTANALYSISANALYSIS



� Computers only see a set of numbers. It is necessary to interpret their meaning.

RAW data Processing information Interpretation



� Colour Spaces:�RGB: Colour cube. It is used by electronics devices to

represent colours.

�HSL: Colour cone. Luminance (L) is independent from Chrominance (H+S)

�H � Hue�S � Saturation�L � Luminance



� In those colour spaces (RGB, HSL…) it is no easy to measure colour similarities

� To create a color space that represents color information as humans see it the CIE Lab space was created

� 3 Parameters: Luminance, ‘a’ and ‘b’ � Advantages: Color difference applying Euclidean Distance

RGB_distance = (R1-R2)^2 + (G1-G2)^2 + (B1-B2)^2 �� It does not give a color similarity ideaLab_distance = (L1-L2)^2 + (a1-a2)^2 + (b1-b2)^2 �� It gives a color distance as humans perceive

similar colors.



� It is possible to detect non visible characteristics for humans. Some animals as insects or deer have a ultraviolet vision that humans can’t perceive

Visible vs. UV



� Near Infrared:�It could be necessary an extra light source�Great penetration

� Mid Infrared �Thermo graphic images

� T – Beams�Higher penetration�Non ionizing



� X Beams, Gamma beams…�Here, it is not processed what material reflects, but

what passes through the object and hits into a specific surface

�Due to their high energy and penetration capacity, they are ionizing



� Spectrography:� A diffraction net is used to split the light into its different

wavelengths

� Each image pixel is defined by an N-dimension vector where each n-component represent a different wavelength.

� Uses:

�Materials, food, pathogens… classification

�Health�Aerial inspection

Spectrophotometer One pixel spectrumHyper spectral cube



� Image Types:

Multidimensional imageImage sequence (video)

3D ImageFloat point image



� Stereoscopic Vision Systems:� It uses 2 or more cameras.

� It needs that all the cameras watch the same scene

�Locating the same point (x, y) in each image it is possible to triangulate the 3D real Point (x, y, z)

�Epipolar points are calculated in calibration step

3D Vision: Stereoscopic


� Stereoscopic Vision:�Stereovision platforms needs to be calibrated

�A chessboard patter is used to calibrate the platform. It needs several images along the working area

�Stereovision platforms based in two cameras try to simulate the humans sight sense

3D Vision: Stereoscopic


� Laser Beam Triangulation:

�A laser plane is projected into a region of interest�The intersection between the laser and the surface of

interest creates a shape�This shape is shown from an external camera and is

used to get the 3D information from one section of the object

3D Vision: Laser triangulation


� Laser Scan:�There are differet ways to get a laser scan for surfaces:

�By movin the object or the set camera + laser

�By using a mirror to get and aditional degree of fredom moving the laser beam

3D Vision: Laser triangulation


� Structured Light:�Projects a pattern of light on the surface and look at the

deformation of the pattern on it. The pattern may be one dimensional (line) or two dimensional (grid).

�Needs Calibration. The pattern is projected on a calibration object. Watching how the patter is projected on the surface and comparing it with the expected deformation the system is calibrated

3D Vision: Structured Light


� What’s biometrics?�A technology.�Can make a Relationship between a Person and a

previously extracted Pattern.�Allows users to Authenticate.

� What does this mean?�You need not know anything (Passwords)�You need not have to anything (Tokens)�All you have to do is BE there

Biometrics: Overview

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� Identification

� Authentication

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Peter

John

Template

Peter

DataBase

Peter Mary Peter

Peter

Am I Peter ?

Whoam I??

Am I Peter ?

Biometrics: Overview


� Several terms are used�FAR (False Acceptance Rate)

�% of the users accepted by the system when they should not have been accepted.

�FRR (False Rejection Rate)�% of the users who are rejected by the system when they

should have been accepted.

�ERR (Equal Error Rate)�The point in which FAR and FRR have the same value

�We can work at different levels of security setting a threshold to accept or to reject the doubtful cases

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Biometrics: Definitions


� It is based on the extraction of fingerprint characteristics

�Minutiaes

� Advantage:�Constant in time�Good precision

� Disadvantages:�People associate fingerprint with police�It is not the most robust biometric system

Biometrics: Fingerprint


� Feature extraction from the bottom eye’s circulatory structures

� Eye illumination through infrared light� Optic complexity� User profile based on arteries and veins distribution

� Advantage:� Excellent robustness� First commercial system

� Disadvantages:� Intrusive (too much)� Information acquired from the bottom eye

Biometrics: Retina


Biometrics: Iris

� Feature extraction from the iris texture� Eye location� User profile based on texture analysis. All the algorithms for image processing are valid.

� Advantage:� Precision� Several solutions on the market

� Disadvantages:� Intrusive� Could be non collaborative� There exists the Iridology: medicine based on iris analysis


Application to Projects

� Biometrics� Surveillance� Hyperspectral based projects


� Multimodal biometric profile : Several biometric features from one measure.

�Easy for user: only one authentication system�More robust: Different biometric measures to

authenticate for each user

Biometrics: Palmprint recognition


� Palmprint Recognition prototype

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ComputerScanner

Imageadquisition

system

Remote DataBase

TCP/IP I/O card

Biometrics: Palmprint recognition


Biometrics: 2D / 3D face recognition

3D Face Model

Extracted Face Points

� Algorithm main steps:�Face orientation detection�Face Points Extraction�2D Face recognition algorithm


� Multiview worker tracking�Multicamera: One camera for each view�Object detector for each camera�Algorithm for data coherence: fusion of the multicamera

objects detection.�Non collaborative RFID readers: worker identification�Space coordinates transform: from camera space (3D

world) to plane (2D space)

� System features�Restricted areas intrusion detector.�Abandoned object detector.�Morphological detector: Humans / Trucks / Others

Surveillance: worker tracking


Surveillance: worker tracking

� Each camera has its own object detector and plane-space transform algorithm

� With the Plant Locator Viewer it is possible to combine the different camera data with the RFID information for worker identification


� Use of hyperspectral images for material classification.�Distinguish from same colour materials�Detection of “hiden” objects�…

Hyperspectral process

Wavelength (nm)

250 500 750 1000 1250

)(λrLVisible

Spectrum

Hyperspectral pixel


� Materials which seems to have the same colour can be easily distinguish analysing their hyperspectral response

Hyperspectral process


CONCLUSIONS


� Image processing is a “mature” technology, not only a laboratory one.

� Biometric with image processing:

� It is a robust technology.

� It offers a high reliability.

�There are several different techniques.

� Security tasks…

� Image processing can solve lots of surveillance issues.

� Image processing can be applied to a large number of applications

Conclusions


� Image Processing is more than algorithms.

� Optics, lights, mechanical system are at least as important as agood algorithm.

� Use the constrains of the application to simplify algorithms.

Conclusions

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More information:

[email protected]

www.robotiker.com

Vision-Based Technologies for Security in LogisticsINFOTECH is the Unit of ROBOTIKER-TECNALIA...

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