PRE2016 4 Groep3

Group members

Student ID	Name
0900940	Ryan van Mastrigt
0891024	René Verhoef
0854765	Liselotte van Wissen
0944862	Sjanne Zeijlemaker
0980963	Michalina Tataj

Introduction

Problem description

To describe the problem it is usefull to first look at the current situation for we would like to create a model to optimalise the procedure. The procedure we believe can be optimalized a lot and still has some problems in it is the security at airporst. Currentle all the security checks are done by humans, so besides the standard checks everybody gets, the securityguards determine who 'looks suspicious' and needs to be checked upon more thoroughly. This method still has some problems in it. We would like to introduce a model able to detect various illegal/endangering activities such as (preparation of) violence, acts of terrorism, smuggling and stealing before they have a chance to occur. The model will determine whether (a) person(s) is/are acting suspicious by measuring the biometrics during walking and motion patterns, as these can be used to deduct a person’s mental state, like anxiousness . The problems our model will solve are listed below. Their will also be some problems/constraints explained we have to take into account when making the model.

First problem: In an airport it can get very crowed, this will make it a lot harder for the guards to actually see every single person and detemine if they act suspocious or not. Using multiple camara's already improved this, but we believe that using a model which can analyse the camera footage no person will be missed and at least everybody walking trough the airport will be checked upon suspicious behaviour. This should lead to less or no possible criminals getting away at the airport. Because of the crowds at an airport it is seldom possible to capture the motions of the lower body of a person, especially with (stationary) camera's. this will leed to the constraint that the model can only analyse the biometrics of the upper body. Another option would be to look at and optimalize the number of camera's and the angle their are mounted in and the positions, this way it might be possible to capture the motions of the upper and lower body of the persone walking past. But we will not elaborate on this matter.

Second problem: Because Security guards now deside for themselfs if a person is suspicious or not there will always be some ammount of bias and/or (instructed) profiling. Since our model will analyse a persons behaviour and not it's appaerence it will be a lot more objective and will not use profiling upon appearance. The only thing which needs to be taken into account for this is to check if different types of behaviour are globaly and cultural consistend, does globally everybody show the same behaviour whenthey are for example nervous? Since at the airport people for all different country's and cultures will be present this will lead to the constraint that the model should only use globally consistend que's to determine wheater or not a person is suspicious.

Definitions

Abnormal behaviour

Abnormal behaviour is defined as behaviour that deviates that of the standard behaviour of people in a specific context and is not frequently observed. In the chapter 'Suspicious behaviour' goes more into depth of how we define and detect this abnormal/suspiscious behaviour.

Biometrics

We define biometrics as the measurements and analyses of body movement characteristics that are unique to types of behaviour. see the chapter 'Possible biometrics for detecting abnormal behavior in crowds' for more in depth information of how we determine the biometrics from surveillance footage

Objectives (/ TO DO list)

Goal: Develop a model for a video-based abnormal behaviour detection program

Objectives of this project:

• Formulate concrete problem statement
• Develop overview of the State-of-the-Art
  • List of possible biometrics for detecting abnormal/suspicious behaviour
  • List of different methods available for measuring biometrics (pros/cons, what method works best for what purpose/setting)
  • Current areas of research
  • Problems with current technologies
• Develop model scenarios for determining abnormal behavior
  • Determine what constitutes abnormal behavior (heavily dependent on context)
  • What techniques could be used (pros/cons, possible new ideas)
• Develop USE aspects
  • Users:
    • Develop easy-to-understand graphical interface for primary users
    • Maintain sense of participation in primary users
    • Conduct survey among general public to research support of such an application and to probe stance on privacy vs security
    • Incorporate findings into design
  • Society:
    • Look into societal advantages (decreased criminal/terrorist activity, global sense of security, decrease in racial/religious tensions)
    • Look into societal disadvantages (decrease in (perceived) privacy)
    • Incorporate findings into design
  • Enterprise:
    • Make sure model is economically feasible and can compete with current systems
    • Look into advantages/disadvantages for enterprises
    • Incorporate findings into design
• Finalize actual model design
• Create final presentation

The main goal of the model is to provide a general structure of a program which is capable of identifying suspicious persons for security applications. The method should be based on biometrics which can be used to determine abnormal behavior in order to obtain a higher success rate than comparable human-based surveillance.

The objectives of the model are:

• Technical objectives:
  • Decrease false-negative rate compared to human-based surveillance
  • Decrease false-positive rate compared to human-based surveillance
  • Provide results to primary user(s) (security guards/police)
• USE objectives:
  • Users:
    • Provide easy-to-understand information to primary user
    • Provide a higher sense of security (secondary user)
  • Society:
    • Decrease terrorist activity
    • Higher global sense of security
    • Higher crime prevention
    • Decrease racial/religious tensions
  • Enterprise:
    • Create a system which is better than current systems, in order to sell to users
    • Be economically feasible
    • Decrease damage caused to assets (such as buildings) and maintain company reputation

State-of-the-art

Possible biometrics for detecting abnormal behavior in crowds

In order to be able to detect abnormal behavior certain characteristics are required in order to identify agents in a a scene. Such characteristics are based on either physiological or behavioral characteristics and are generally referred to as biometrics. In order to asses the biometric the following conditions can be used:

• Universality (every person in the scene should posses the trait)
• Uniqueness (it has to be sufficiently unique so that the agents can be distinguished between one another)
• Permanence (the trait should not vary too much over time)
• Measurability (the trait should be relatively easy to measure)
• Performance (relates to the speed, accurateness and robustness of the technology used)
• Acceptability (the subjects should be accepting towards the technology used)
• Circumvention (It should not be easy to imitate the metric)

Most research on identifying behavior via computer vision techniques are focused on non-crowded situations. The subject is either isolated or only a very small number of people are present. However, most of the conventional computer vision methods are not appropriate for use in crowded areas. This is partly due to the fact that people display different behavior in crowd context. As a result, some individual characteristics can no longer be used, but new collective characteristics of the crowd as a whole now emerge. Another big factor is the difficulty of identifying and tracking individuals in a crowd context. This is mostly due to occlusion of (parts of) the subject(s) by objects or other agents. The quality of the video image and the increased processing power needed to track individuals are also important factors.

Most current research on detecting abnormal behavior in crowds focuses on tracking of the people in the crowds. The individual tracking of people has proven to be difficult in a crowd context. Many different methods have been proposed for individual tracking and while these tend to work satisfactory for low to moderately crowded situations, they tend to fall flat in higher density crowds. There are also models which try to use general crowd characteristics to detect anomalies, but these tend to ignore singular abnormalities and are better suited for detecting general locations in the scene which contain anomalies, for example where a fire has broken out.

There are promising models that try to combine a bit of both extremes. There is a model which uses a set of low-level motion features to form trajectories of the people in the crowd, but uses an additional rule-set computed based on the longest common sub-sequences . This results in a system that is capable of highlighting individual movements not coherent with the dominant flow. Another paper created an unsupervised learning framework to model activities and interactions in crowded and complicated scenes . They used three elements: low-level visual elements, "atomic" activities, the most fundamental of actions which can not be further divided in sub-activities, and interactions. This model was capable of completing challenging visual surveillance tasks such as determining abnormalities.

Common problems in crowded scenes, such as occlusion of the subjects, can be prevented by moving to a multi-camera surveillance system. Having different angles of the same scene available allows the system to better identify and track subjects. Dynamic cameras (cameras able to turn and zoom in and out) should be able to increase the efficiency of identifying suspicious persons by for example zooming on on the area. However, the use of multiple cameras brings new problems with it. It is difficult to calibrate camera view with significant overlap and to compute their topology. Calibrating camera views which are disjoint and where objects move on multiple ground planes has proven to be challenging. Most research on video surveillance assume a single-camera view, even though multiple-camera surveillance systems can better solve occlusions and scene clutters. Most research on multi-camera systems are based on small-camera networks.

Detecting human activity

In order to recognize human activity, a general system is used which divides human activity recognition in three levels. The low-level represents the core technology, meaning the technical aspects for recognizing humans in a scene. The mid-level represents the actual human recognition systems. The high-level represents the recognized results applied in an environment, for example a surveillance environment.

The low-level contains three main processing stages: object segmentation, feature extraction and representation, and activity detection and classification algorithms. Object segmentation is performed on each frame in the video sequence to detect humans in the scene. The segmentation can be divided into two types based on the mobility of the camera used. In case of a static camera, the most used segmentation method is background subtraction. In background subtraction, the background image without any foreground object(s) is first established. The current image can then be subtracted from the background image to obtain the foreground objects. However, this process is highly sensitive to illumination changes and background changes. Other more complex methods are based on complex statistical models or on tracking. For dynamic cameras the background is constantly changing. The most commonly used segregation method is than temporal difference, the difference between consecutive image frames. It is also possible to transform the coordinate system of the moving camera based on the pixel-level motion between two images in the video.

The second stage of the low-level looks at the characteristics of the segmented objects and represents them in some sort of features. These features can generally be categorized in four groups: space-time information, frequency transform, local descriptors and body modeling. Different methods are used for the different categories. The classification algorithm is based on the available set of suitable feature representations.

The actual mid-level abnormal activity recognition generally relies on a deviation approach. Explicitly defining abnormal behavior depends heavily on context and surrounding environments. These types of behaviors are, by definition, not frequently observed. Thus most models use a reference model, as in the case of background subtraction, based on examples or previously seen data, and consider new observations as abnormal if they deviate from the trained model. Different methods are used. The last level, high-level, represents the actual application. The application is dependent on the environment of the system. This research focusses on surveillance environments. In surveillance systems, human activity recognition is mostly focused on automatically tracking individuals and crowds in order to support security personnel. These types of environments tend to have multiple cameras, which can be used together as a network-connected visual system. The cameras can than track the position and velocity path for each subject. The tracking results can then be used to detect suspicious behavior.

Suspicious behaviour

In order to teach our software to recognise suspicious activity we must first determine what constitutes such behaviour. Trying to remain inconspicuous while conducting a highly suspicious action results in a behavioural paradox that can be difficult to detect by bystanders. However, there are some general patterns in body language and motion that are observed significantly more frequently in individuals with criminal intentions. In our research we will distinguish between two types of non-verbal cues: motion of the body itself and motion of the individual through a crowd. With ‘body’ we refer to the torso, head and arms, because these are mostly visible in a crowd, while the lower body is not. Facial expressions are outside the scope of our project and will not be taken into account.

Body language

Body language of people with criminal intent tends to differ from that of bystanders, because they need to remain undetected . Frey , among others, showed that people recognise this deception far more often than can be accounted for by chance.

During the build-up phase of a crime, offenders often show an increased frequency of object- and self-adaptors, in other words, the “manipulation of objects without instrumental goal” and the frequency and duration of contact between the hand and the own body . This includes touching and scratching of the own hair and face and strictly unnecessary contact with carried objects, such as tapping pens repeatedly or reaching for an object multiple times without using it. This behaviour was observed in both assassination and bomb-planting scenarios in large crowds, indicating that it is likely not crime-specific .

Troscianko et al. observed that head orientation could also give away suspicious intentions. Offenders look away from their walking direction more often and look around repeatedly. However, one should be careful when considering these signs, as airports are vast and crowded, which often results in passengers getting confused or lost. Their searching behaviour could result in similar head movement, while they have no harmful intentions.

Therefore, in addition to the cues itself, a reliable method is needed to differentiate between real cues and normal behaviour. One way to do this is by measuring behaviour relatively to the crowd. To ensure that one wrong gesture does not lead to a false positive, a baseline is established first. The frequency of suspicious behavioural cues is measured in the crowd overall to determine what should be regarded as ‘normal’ behaviour . Only distinct deviations from this baseline are considered suspicious.

The recognition of body language does not rely on perfect information and vision. Experiments were conducted with recognising human behaviour based on point light animation footage. It was observed that humans still can pick up behavioural cues with this limited visual information . This supports the idea that computer software will be able to pick up behavioural cues, despite its visual limitations.

Motion patterns

Criminal intentions do not only show though a person’s body movement, but also in the way they move through a crowd. In general, an offender will show a significantly more abrupt kind of movement during the build-up phase of a crime. There are more changes in speed, position and direction than in a general crowd .

However, it is important to keep in mind that these movement patterns should be observed within the relevant context. For example, in an airport, changes in speed and direction could also indicate searching behaviour. It is therefore import to study movement that deviates from the rest of the crowd, rather than universal ‘suspicious’ movement.

All cues, both for motion and body language, were found to be positive deviations, i.e. the behaviour was expressed more strongly by the culprit than by the bystanders . This is a useful property for our project, as it is easier to spot the deviating behaviour of one individual than to find a behaviour that occurs in the overall crowd, but not in one suspicious individual.

Approach

Planning

In order to keep track of the progress of the project and set deadlines for our goals we have made a Gantt chart. This chart shows what tasks are done during what time and how these tasks are divided among our resources.

Milestones

We consider several milestones based on the tasks that lay before us as can be seen in the Gantt chart in the Planning section:

• Finished the research into what defines abnormal Behaviour. (planned by the end of week 2)
• Finished the research into the existing methods for biometric scanning. (planned by the end of week 3)
• Finished analysing the USE aspects that our project brings with it. (planned by the end of week 3)
• Having developed a model for the detection of abnormal behaviour based on previous research and analyses. (planned by the end of week 6)
• Holding the final presentation presenting our product.(planned by the end of week 8)
• Finalized the wiki for judging. (planned by the end of week 8)

Deliverables

At the end of the project we aim to produce the following deliverables:

• A software model of a biometric scanner that detects suspicious behaviour
• Full documentation of the development and research process on this wiki
• A final presentation explaining said model and process
• A peer review of all group members

USE aspects

User

Primary users

• Security Guards
• Police officers
• Military personnel

Secondary users

• Persons being filmed
• Airline companies
• Technical maintenance personnel

Tertiary users

• The people manufacturing, repairing and designing the product
• Governement

User friendliness

User friendliness can be described using the following factors:

• Learnability:

The system does not require the user to learn many new skills. The only thing the user (airport security) needs to learn is how the system lets them know when a suspicious person is detected and who/where this person is.

• Efficiency:

Once the system is in use, a higher level of efficiency will be reached since more criminals will be detected and less innocent people will be checked by security.

• Memorability:

Since there is not a lot to learn for the user, it will be possible to use the system even after not using it for a longer period of time.

• Errors:

Once the system is realised, its error rate should be analysed extensively. If it made more incorrect detections than a human securtity guard, the system would be unnecessary. For the effectiveness of the product it is important to keep te error rate of the system as low as possible.

• Satisfaction

With the questions of the interview below, a general idea of what the users are looking for in the system can be established and interpeted. However, since the survey will be restricted to only a few individuals we cannot guarantee that there results are representative. If the system was realised, user tests would still be needed during and after the design process to find out what the users think of the ineraction with the system and which things should be changed to suit their needs optimally.

Interview primary users

In order to gain insight in the current methods of detecting suspicious persons and to look at the wishes of the primary users of the system, a survey will be done with security personnel at airport Veldhoven. We will ask them the following questions:

Behaviour:

• Which behavioural cues do you look for when trying to identify suspicious persons?
• What kind of criminal activities do you encounter at the airport?
• Do different criminal activities correspond to different behaviours?
  • Could you explain which cues are specific for each crime?
• To what extend do you look at body language specifically?
• Do you consider the way people move through a crowd when trying to identify suspicious individuals?
  • How does the motion pattern of a suspicious person differ from that of an innocent person?
• What role does facial recognition play in your job?

Procedure and failure rate:

• How many people are present in the departure hall during peak hours?
• What actions do you take after identifying a suspicious person?
• How often does it occur that an apprehended person turns out to be innocent?
  • Which specific causes contribute to this false-positive rate?

System interaction:

• To what extend would you trust a camera-based computer system which detects suspicious persons automatically?
  • Why would you (not) trust it?
• If such a system existed and it detected a suspicious individual, how would you prefer this information to be presented to you?
  • Why do you find this type of interaction most convenient?
• Are there any particular features you would like to see in such a program?
• Are there any aspects of your job that, in your opinion, cannot or should not be automated?

In the end, we could not arrange a meeting with a security guard from the airport. Instead, we interviewed a teacher from Summa college that trains security guards. The interview was conducted in Dutch and we left it that way to make sure we did not interpert anything wrong during translation.

Naar welke gedragskenmerken kijkt u om verdachte mensen te herkennen?

Dat is heel breed, zeker op de luchthaven. Een voorbeeldje: er zijn mensen die hebben haast. Op een luchthaven hebben altijd mensen haast. Dat zou opvallen, maar op de luchthaven valt dat weer niet op, omdat dat normaal is. Mensen die niet weten waar ze moeten zijn gaan vaak informatie inwinnen. Staan ze ergens anders te kijken en weten ze niet waar ze moeten zijn, dan is dat een afwijkende situatie. Je kunt ook denken aan mensen zonder koffer of mensen in de zomer met een lange jas aan; kleding die niet past in de tijd van het jaar. Overmatige transpiratie als het vriest, zou ook een indicator kunnen zijn. Bepaalde groepen die bij elkaar zijn, bijvoorbeeld heel veel mannen bij elkaar. Dat wil niet zeggen dat er altijd iets mis is. Je moet er ook naar kijken of het bijvoorbeeld een zakelijke vlucht is of een vakantievlucht.

Kijken jullie ook naar kleine dingen, zoals hoe vaak mensen bijvoorbeeld hun gezicht aan raken?

Ja, dat kan. Het is vooral een stukje houding. Er is onderzoek naar gedaan: je hebt een aantal oerinstincten en iedereen reageert vanuit zijn oerinstinct. Je hebt daar geen invloed op, dus als je zenuwachtig bent, laat je dat altijd op een of andere manier zien door middel van houding of gedrag. Wij kijken naar wat de normale situatie is. Wijken mensen daarvan af, dan is er iets aan de hand, maar dat wil nog niet zeggen dat het fout is.

En als mensen tegen de stroom in lopen?

Dat kan inderdaad ook verdacht zijn. Waarom loopt iemand tegen de stroom in?

Wat voor criminele activiteiten zien jullie zoal op een vliegveld?

Mogelijke terroristische activiteiten, mensen die illegaal reizen, mensen- en drugssmokkel. Maar in basis: al die mensen weten dat ze fout zitten, dus die primaire reactie zie je altijd.

Kun je ook zien aan de hand van de reactie wat voor criminele activiteit iemand van plan is uit te voeren?

Er zit zeker verschil tussen drugssmokkel en het voorbereiden van een aanslag, maar dat is niet mijn specialiteit.

In hoeverre kijkt u naar lichaamstaal?

Heel veel. non-verbale communicatie is zeker 90%. Je kunt niet iedereen aanspreken: “Hé, ga je drugs smokkelen?”. Je gaat altijd af op lichaamskenmerken.

Geldt dat ook voor mensen die naar camerabeelden op schermen kijken of is dat met camera’s lastig te zien?

Nee, voor zover ik weet gaan die van dezelfde kenmerken uit.

Hoe kijk je naar mensen die zich anders door een menigte bewegen?

Ze lopen in een andere richting, staan stil. Je kunt ook werken met triggers. Voorbeeld: bij Schiphol hebben ze zichtbaar een aantal marechaussees neergezet bij de ingangen en gekeken hoe mensen reageerden. Op de camerabeelden kun je dan zien dat mensen soms een omtrekkende bewegen maakten en via een andere kant naar binnen gingen. Niet iedereen die dat doet is iets van plan, maar ze hebben wel een bepaalde natuurlijke reactie op uniformen, dus er is altijd iets aan de hand.

In hoeverre speelt gezichtsherkenning een rol?

Steeds meer, ook in verband met big data en terrorisme. Als er verdachte personen zijn, staan die in het systeem en als ze ergens opduiken, worden ze geregistreerd. Het lastige is, dat sommige landen niet met elkaar samen willen of kunnen werken, maar dat gaat wel komen. Er zij nu al systemen die alles meten: gezicht temperatuur, transpiratie, vingerafdrukken, maar wat doe je met die informatie? Er zit een bedrijf in Weert, UTC, dat daar veel mee doet. Zij zouden jullie daar misschien meer informatie over kunnen geven.

Hoeveel mensen bevinden zich gemiddeld in een vertrekhal?

Dat ligt heel erg aan het vliegveld. Bij Maastricht is dat zo’n 200-300 man, maar bij Schiphol moet je aan duizenden mensen denken. Dat is ook interessant voor jullie systeem: ga je die allemaal scannen of pak je alleen risicovluchten?

Wat voor actie onderneemt u als u een verdacht persoon herkent?

We spreken die persoon aan. Waarom is jouw gedrag afwijkend van de norm? Dan zeggen ze bijvoorbeeld: “Ik heb mijn teen gestoten, daarom loop ik moeilijk.” Dan ga je er dieper op in en daaruit ga je filteren: vertelt hij de waarheid? Zo niet, dan is er iets aan de hand.

Hoe vaak komt het voor dat een persoon die aangehouden wordt, onschuldig is?

Daar heb ik geen inzicht in, zeker niet op de luchthaven. Het mooiste zou zijn 90%, dan wordt er hard gewerkt en weinig gevonden.

Is het dan niet vervelend voor onschuldige personen dat ze staande gehouden worden?

Het punt is: hun gedrag wijkt af. Als je alleen gedrag aan gaat pakken van mensen die schuldig zijn, mis je heel veel dingen. Je kunt dus beter heel veel mensen eruit pikken en erachter komen dat ze zich op een bepaalde manier gedragen zonder kwaad in de zin te hebben.

Zijn er specifieke oorzaken die tot het verdenken van onschuldige personen leiden?

Haast en onwetendheid bijvoorbeeld. En spanning – mensen zijn gespannen omdat ze met een vliegtuig de lucht in gaan. Die kenmerken zijn hetzelfde als van iemand die iets kwaads in de zin heeft.

In hoeverre zou u het systeem dat wij beschrijven vertrouwen?

Ik denk dat je daar goed op kunt vertrouwen. De ene beveiliger vindt een situatie wel verdacht ende andere niet. De ene dag heeft hij goed geslapen en de andere niet. Dat zou niet mogen, maar het gebeurt wel. Als een systeem een gedeelte van zijn keuzes kan maken, zou dat ideaal zijn.

Hoe zou u deze informatie het liefst gepresenteerd hebben?

Dat is lastig, want je hebt mensen die rond lopen. Het zou kunnen met een mobiel scherm op de telefoon of met een meldkamer.

Zijn er nog specifieke dingen die u terug zou willen zien in zo’n programma?

Nee, dat durf ik zo niet te zeggen. Ik denk dat het heel uitgebreid kan en mag.

Zij er aspecten die niet door het systeem overgenomen zouden moeten worden?

Nee, in het systeem zoals je nu beschrijft niet. Er is nog steeds een stukje aanspreken van mensen. Dat moet denk ik wel blijven. Je moet er toch achter zien te komen waarom iemand zich zo gedraagt. Iemand kan zweten omdat het dertig graden is of omdat hij een bomgordel om heeft: hoe kom je daar achter met een systeem?

Society

Advantages

For a System to work and be accepted in society it should have a lot of societal advantages. The biggest advantages and thus the reasons to design this system will be listed below.

Terrorist/criminal activity

The first advantage is also the main goal of the system. To detect criminal activity at airports. By using cameras and algorithms to detect movements linked to nervousness, and movents labled as 'suspicious' potential terrorists and/or smuglers etc. can be caught. By using this system the process will be more efficient since it can analyse every person walking in the airport. Therefore, this system will be responsible for a higher criminal detection rate and thus reduce the chances of terrorist attacks.

Security

As said above, using the system should result in a higher criminal and terrorists catching rate, making the airport and flying safer. The system will not act as a replacement of the security at an airport, but will be an aid to help them be more accurate, select/check less hermless people, and more effective, possible to check every person entering the airport, in finding criminals. Since the system is only capable of detecting suspicious persons the securtity/police will still have to the check for proof and, if neccesary, conduct the arrest.

Racial/religious tensions

The last advantage this sytem will provide is its objectiveness. Since the system will be 'scanning' persons based on their behaviour and movements the outer appearance of the person is not taken into account when determmining if a person could be dangarous or not. With persons/security/police detecting suspisous persons their will always be some part of bias, since it is humanly impossible to be completely unbias. Also currently selecting people is also partyally based upon profiling, by looking for external characteristics convicted criminals have in common and based on those external charasteristics search for people who also have these charateristics because it would than be more likely for them to be a criminal. This is a self induced system. If for example 70% of convicted criminals would wear blue nail polish, people wearing blue nail polish would be checked upon for more often than people who don't, leading to more findings and more arrests of people with blue nail polish and thus keeping up this profile. Currently there are a lot of discussions about this fenomenon, because it is claimed to happen upon characteristics such as race and religion. This leads to a lot of tension between different races and religions within a country but also world wide. By using the system this (racial) profiling can be extermintated since it is not based upon a database of suspicious external charateristics but upon behaviour. A lot of our behaviour and body language is unconsiouly so by using psychological research this unconsious behaviour can be analysed and liked to certain feelings and acts, such as nervousness and lying.

Disadvantages

Besides the advantages their should also be looked closely to the disadvantages it might have for the society.

Privacy concerns

The biggest disadvantage people will propably bring up will be the privacy invasion. Knowing that the moment you walk into the airport you're beging filmed and watched will cause the issue that other people will know where you are, when you leave the country an where you're going to. In the privacy vs. security debate there are three questions that need to be awnsered to determine is the advantages outweight the disadvantages. (Brey,2004) these three questions are: How much added security results from the system? How invasive to privacy is the technology, as can be judged from both public response and scholarly arguments? Are there reasonable alternatives to the technology that may yield similar security results without the privacy concerns?

the first question cannot be awnsered yet, because we can't yet measure if and how much more criminals will be caught with the system, therefor it should first be build and testes before qe can get these results. For the second question an awnser could be that is doesn't change much to privacy since at an airport there now already are security camera's so people already are being filmed, they are just not being analysed by an algorith yet. To get more insight in how the public thinks about this a survey could be held. the anwser to the third question is also debatable because of the word reasonable. Because the method used now, analyzig by security guards, can be seen as an resonable alternative since it is also acceptec now. But one could also reson ther isn't a resonable alternative because no people could be completly without bias. And the system will be more effective than the current method, althought as stated in the first question it can not be determined how mich more effective it will be.

Errors

Another disadvantage that should be taken into account is the errorrate of the system. Since it is very hard to test the system in simulation situations, unconsious behaviour is hard to simulate/act. So the real errorrate could only be detemined when tested in real life, this could be risky is it than turns out to have a large errorrate by either selecting a lot of harmless people, or by not selecting potential criminals.terrorist. The first type of error doesn't bring much risk, it is only inconvenient vor the selected travlers. The second type of error could be risky since then criminals and terrorist aren't getting cought and can still cause trouble. A way to reduce this risk and handle this disadvantage is to when it still needs to be tested the reagular security as it is done right now will run simulatiously. This way during the testing it will be atleast as save as it is right now.

Enterprise

Feasibility

The proposed system is quite easy to implement. No new infrastructure is needed as the system will use the existing monitoring network. However it will be necessary to install the program that will analyse the camera recordings into a computer. This computer should be strong enough to support required hardware. As the regular CCTV monitoring systems do not requisit the assistance of the computer, this will be an additional cost for the implementation of the new network.

Advantages

The proposed system can be quite advantegous for the enterprise. By increasung safety of the passangers and employees of the airlines and airports, moe people will be willing to use this way of transport which then will be beneficial for business of both airline companies and stores at the airports. By increasing safety of the personnel, the job in this line of work will be more appealing. Another adventage is the decrease in damage of the company's property, like aircrafts or airport building which occurs in case of a bomb detonation. The system will decrease losses for the companies, as well as increase income by ensuring safety of the clients and thus, enhance competitiveness of this way of transport.

Disadvantages

Despise the rise of potential income, the system will require some additional costs. The hardware will require mainatainance as well as adjustments. Both can only be carried out by the specialized personnel, which will enlarge the costs.

Model

Conceptual model

In summary, we found the following behavioural cues that indicate suspicious behavior:

• Non-instrumental use of objects/fidgeting
• Looking around
• Looking away from walking direction
• Touching your own body (face/hair)
• Deviation from general crowd flow
• Walking in circles

One ‘offence’ should not lead to immediate action. There must be a baseline to determine what level of offence is suspicious. Because the system we want to design is completely new, we have no given baselines that we could use. However, we could implement a learning system that could generate a baseline. The software would be placed in a departure hall of an airport and detect the indicated behaviour. However, it should not report to security yet. It merely counts the amount of offences to establish the average number of behavioural cues per individual. Once we have established a base line and the system is running, we will use probabilistics to determine whether a person is suspicious or not, as we will specify below. Since the system detects the cues to spot suspicious behaviour anyway, it can adjust the baseline actively to adapt to possible changes.

Assumptions

Several things are assumed when creating the model:

• There is only one suspicious person at the time
• The only behavioural cues for suspicious behavior are the ones mentioned above
• The behavioural cues are positive deviations
• All cues have an equal weight

Entities and properties

Security guard:

• Accuracy
• Efficiency
• Bias

Passenger:

• Below baseline behavior

Suspicious person:

• Suspicious behaviour

Software system:

• Efficiency
• Accuracy
• Objectiveness

Airport:

• Crowd density
• Camera placement

Relations

The diagram below illustrates the proposed model and the relations between agents.

When to report

The outcome of the video footage analysis needs to be processed so the system is able to make a decision whether or not a person should be considered suspicious.

The output of the video footage analysis will give us for each behavioural cue how often they occur. This output will be the input of our probability model. For our model, we assumed all cues to be equally important. However, this could easily be adapted by assigning a weight to each cue. The model will consist of a function which adds the occurrence of all cues multiplied by the assigned weight, and then give a so called suspicious score, which indicates how suspicious a person is. Such a function would look like the following:

W₁ C ₁ + w₂ C₂ +... + w_n C_n = S

In this function w_i is the weight assigned to a certain cue, C_i is the amount of times/duration of the cue we got from the video footage analysis, and S is the suspicious score. The model then compares this score to the baseline and if the score is significantly higher than the baseline it should alarm the guard. This means that the model should also create a baseline.

The baseline of the model can be determined by letting the model run for a while and plot the results, these results will probably look like a normal distribution with a mean [math]\displaystyle{ \mu }[/math] and standard deviation [math]\displaystyle{ \sigma }[/math]. This distribution fits well, since the chances of somebody being close to the baseline are highest and higher numbers of behavioural cues are increasingly unlikely to occur.

To find deviations the baseline, we will use the concept of Bayesian surprise. Say our system starts with a given probability distribution of the suspicious scores. Every new event, every new score measured, slightly changes the probability distribution of the system. If the number is close to the previous score, there will be no significant change. However, if a score has a large deviation, it is more ‘surprising’ and will have a larger impact on the distribution . For our system, this means that a person behaves significantly different, hence is suspicious.

We will use a Matlab toolkit that implements Bayesian surprise that can be downloaded here. The model takes into account the series of previous measurements to calculate how surprising a new score is. You can manually set the decay factor that regulates the decay of your believe in a measurement over time. This way, if the general behavior of a crowd slowly changes over time, the system will adapt to this.

We ran a simple test to see whether the program behaves as expected. We used decay factor 0.4 and the following sample data: 5 5 6 5 4 4 5 4 6 5 7 6 5 6 5 5 4 11 5 6. As you can see, the 18 ^th measurement strongly deviates from the rest. The corresponding surprise graph looks as follows:

There gives a clear peak where the value 11 occurs. This example also shows the importance of a calibration phase. The first few measurements give very sharp peaks, because the system has not seen much data yet, so even small deviations are surprising. After a while, it starts to see the larger picture and small deviations no longer cause big surprises.

Software model

The previous chapter described how to determine what is suspicious and what is normal behaviour based on cues that people present when navigating through/ waiting at an airport. Another crucial aspect is of course extracting the described cues from security footage in real time. For this reason it should not only be able to accurately track persons and som body parts, but it should be able to do so with enough efficiency to deliver results to the security guards in real time. There are a couple of ways to allow the software to perform in real time:

• Simply providing the hardware necessary. This will have no negative effects on the performance of the software, but will have high hardware and maintenance costs.
• limiting the framerate the software uses, this means it does not have to analyse all frames so it can keep up with incoming data easier, but it will affect performance as you are creating gaps in the data.
• Taking a more holistic approach for the estimation of cues in a high density crowd, this is far more efficient than object based tracking, but is unlikely to detect subtle cues like fidgeting. This method could also be combined with object based tracking for better results.

For this project we will first take a holistic approach to do crowd tracking, and we will later add as many extractable behavioural cues as possible within the designated project time and computational capability available to us. For the crowd tracking we will use the Social force based particle advection model as desingned by Mehran et al., however they use it to detect abnormal crowd behaviour as it occurs, while we want to detect the behavioural cues leading up to the event causing the abnormal behaviour in order to prevent the event from happening. We will have to extend the model to help us detect the behavioural cues we wish to detect.

In order to write a model we use some data from the publicly availlable UMN dataset that is applicable for our context, and data recorded from live security cameras . The model is written in MATlab R2017a with Piotr's computer vision matlab toolbox, which is an extension of Matlabs image processing toolbox. The current program can read in a compatible video file and determine the optical flow and with it the particle advection (WIP). You can download the current version of the software here.

Minutes

26-04-2017

• The subject of the project has been chosen and the deliverables and objectives (as found on the wiki) have been determined.

30-04-2017

• Orientary research has been performed to develop a better understanding of the subject and better define our goals.
• A planning and milestones have been determined (see the Approach section)
• A wiki page has been created, including a template for the documentation with the already available information filled in.

03-05-2017

• We have agreed upon a list of questions to ask the security officer at Veldhoven. Research of behavioural cues and biometric scanners has been discussed and is still ongoing. Several sections of the wiki, including the planning and charts, were updated and given a more structured layout.

08-05-2017

• The problem statement and deliverables were discussed extensively to clarify the goal of the project. Tasks for the upcoming week were determined and divided.

11-05-2017

• We met at Eindhoven airport to talk to the security guards. They did not have time for us, but gave us an email address that could help us make an appointment.

15-05-2017

• We discussed the problems with Eindhoven Airport and decided to focus on the model instead if we cannot get an interview in the next week, because it messes up our planning too much if we wait. We then agreed on what needs to be done for next week and divided the tasks.

22-05-2017

• We set the goal to have a 'working' code next week. That is, we want the program to be able to track a person. All other details will be implemented later. We also agreed to look further into the probabilistic model that determines whether behaviour is suspicious. The group was split into two teams to carry out these tasks.

• We received a response from a teacher at Summa college that trains security guards. We made an appointment for tuesday 30-05 to have an interview with him.

12-06-2017

• The software model is now able to attach a certain 'Suspicion' score to each particle based in Gaussian distributions of behavioral characteristics. It can also detect particles with unusual suspicion scores.

• The interview with the teacher at Summa College was conducted and transcribed and is available on the wiki.