Sunday 1 February 2015

I predict a riot

So finally decided on what my dissertation is going to focus on and, drum roll please, it is crowd behaviour. Specifically trying to count out when a crowd is likely to form, how many people are in a crowd, why they are they are in a crowd and dispersal patterns of the crowd. As well as this I will have to take into account the ethics of analysing people in crowds.

The main goals of this is to be able to determine at minimum number/groups of people in crowds. From this I can try to ascertain the situation and evaluate possible outcomes. It would be nice to be able to accurately count the people in a crowd however there are issues with trying to count people close together. If they are too close then an issue arises with counting many people as one person, as well as this the faces may not all be visible or clear enough to use facial recognition or just obstructed. There has been research into counting people in crowds or mapping high density crowds but I have not come across anything that is accurate and versatile between scenarios. The research linked for 'counting people in crowds' has a high success rate of more than 96% but the data set is only three videos. With regards to the link provided in the 'mapping high density crowds' they track motion against a certain threshold and if not over the threshold then it is considered static. This raises the issue of people who are not moving fast enough which can happen in over crowded areas would be considered background. On the other hand things, such as large animals, that move at the threshold could be used in the generation of the map thus making it not accurate. Being able to determine when a crowd is about to form would help determine things such as when a riot or a big event is about to happen( discussed in more detail later).

Below is an image of where people counting could be useful to make sure there is no over crowding. As well as this it could be used to determine best flow of traffic or if something abnormal is happening. *




Within crowds there are specific behaviours that can looked out for to help identify individual people or to try and work out what is going on in the scene. When walking down the street and another person is heading towards you the closer they get the more you move to the side to pass them as explained here. When people are coming from different angles but heading in the same direction they tend to merge in to a single flow of traffic. There is however, no specific detailed definition of a crowd but is defined as 'a large number of people gathered together in a disorganized or unruly way'. A few definitions do exist and share common specifics such as 'conceptualising a crowd as a sizeable number of people gathered at a specific location for a measurable time period, with common goals and displaying common behaviours' which is a extract from this online pdf. This document also goes into more detail about what is expected from a crowd. There is however issues with things such as determining when a group of people are considered a crowd, such as number of people and time spent together/at a location. For these reasons a set definition of a crowd must be made to allow a system to appropriately determine if there is a crowd or if a crowd is likely to form. As well as defining a crowd, definitions of crowds in different situations from data sets will allow the system to more clearly work out what is going on.

The accuracy of this is likely to be incrementally lower as the crowds get bigger as it will be harder to count people and monitoring the flow will become process heavy. As such focusing on each part such as the counting will allow more targeted results with hopefully higher accuracy. This could be useful though for predicting violence in crowds in which some work has been done here or even counting people in areas to avoid overcrowding and injuries.

Below image shows how the faces are not always showing on cameras, this makes it more difficult to count people using facial recognition. *




Now comes the fun part, ethics and what is ok to use, after all we will be looking at humans and their behaviour.The first thing we need to make sure is that the people who are on the video are ok to have themselves used for research purposes and their privacy is protected. Some questions need to be asked such as ones raised in this papers abstract. Questions such as 'Under what conditions should video be presented and to which audiences'. Videos that are used should have the consent of the people in it and should only be used for the purposes they are made for. As well as this there should be no attempt to try and identify persons within the videos unless that is the reason for the videos and you have express permission from all persons involved.


This is just a little overview of the parts of what needs to be discussed and will be discussed in more detail over the coming weeks.





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